diff --git a/README.md b/README.md
index 3dd52fd8..d6fcc98b 100644
--- a/README.md
+++ b/README.md
@@ -77,3 +77,40 @@ prefix = your_s3_prefix
 ```
 
 [AWS configuration and credential files](https://docs.aws.amazon.com/cli/v1/userguide/cli-configure-files.html) can be used for authentication and settings. Authentication credentials should be specified in `~/.aws/credentials`. Settings like `region`, `output`, `endpoint_url` should be specified in `~/.aws/config`. Multiple different profiles can be defined and the specific profile to use can be specified in the `aws` section of the `.ini` file.
+
+### Loading From MLflow
+
+Simplexity provides a high‑level loader to reconstruct models and read run data from MLflow.
+
+Quick start:
+
+```python
+from simplexity.loaders import ExperimentLoader
+
+# Use your MLflow run ID and tracking URI (e.g., Databricks)
+loader = ExperimentLoader.from_mlflow(
+    run_id="<RUN_ID>",
+    tracking_uri="databricks",  # or None to rely on env MLFLOW_TRACKING_URI
+)
+
+# Load saved Hydra config and inspect
+cfg = loader.load_config()
+print("Model target:", cfg.predictive_model.instance._target_)
+
+# Discover checkpoints and load the latest model
+print("Available checkpoints:", loader.list_checkpoints())
+model = loader.load_model(step="latest")
+
+# Fetch metrics as a tidy pandas DataFrame
+df = loader.load_metrics(pattern="validation/*")  # glob filter optional
+print(df.head())
+```
+
+Notes:
+
+- PyTorch models: if your run used a PyTorch model (e.g., `transformer_lens.HookedTransformer`), ensure the package is installed in your environment. The loader first tries JAX’s `PredictiveModel` path, then falls back to `torch.nn.Module` and sets `model.eval()` by default.
+- Persistence: the loader reconstructs the persister from the saved config. If the run has no persistence or no checkpoints, `load_model()` raises an informative error.
+- S3 credentials: if your persister uses `S3Persister.from_config`, you can override the location of the `.ini` via `ExperimentLoader.from_mlflow(..., config_path="/path/to/config.ini")`.
+- Metrics filtering uses glob syntax (e.g., `"validation/*"`).
+
+See `notebooks/experiment_loader_demo.ipynb` for a runnable example.
diff --git a/notebooks/experiment_loader_demo.ipynb b/notebooks/experiment_loader_demo.ipynb
new file mode 100644
index 00000000..91ffc6b1
--- /dev/null
+++ b/notebooks/experiment_loader_demo.ipynb
@@ -0,0 +1,396 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# ExperimentLoader Demo\n",
+    "\n",
+    "This notebook demonstrates how to use `ExperimentLoader` to load and analyze MLflow experiments, including configuration, metrics, checkpoints, and trained models.\n",
+    "\n",
+    "## Prerequisites\n",
+    "\n",
+    "- **MLflow Access**: Databricks credentials (host/token)\n",
+    "- **S3 Access**: AWS credentials and config.ini for checkpoint loading\n",
+    "- **Dependencies**: Model packages (e.g., transformer-lens)\n",
+    "\n",
+    "## Usage Patterns\n",
+    "\n",
+    "### Basic Usage\n",
+    "```python\n",
+    "from simplexity.loaders import ExperimentLoader\n",
+    "\n",
+    "# Initialize loader\n",
+    "loader = ExperimentLoader.from_mlflow(\n",
+    "    run_id=\"your-run-id\",\n",
+    "    tracking_uri=\"databricks\",\n",
+    "    config_path=\"path/to/config.ini\"  # Optional: for S3 checkpoints\n",
+    ")\n",
+    "```\n",
+    "\n",
+    "### Load Different Data Types\n",
+    "```python\n",
+    "# Configuration\n",
+    "config = loader.load_config()\n",
+    "\n",
+    "# Metrics (all or filtered)\n",
+    "all_metrics = loader.load_metrics()\n",
+    "val_metrics = loader.load_metrics(pattern=\"validation/*\")\n",
+    "\n",
+    "# Checkpoints\n",
+    "checkpoints = loader.list_checkpoints()\n",
+    "latest_step = loader.latest_checkpoint()\n",
+    "\n",
+    "# Model (requires checkpoints + dependencies)\n",
+    "model = loader.load_model(step=\"latest\")\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Example: Loading a Transformer Experiment\n",
+    "\n",
+    "Let's walk through loading a complete experiment."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "🔍 Run ID: 138baafac5a34726ab070bb1be72a83d\n",
+      "📍 Tracking URI: databricks\n",
+      "🔧 Config: /Users/adamimos/Documents/GitHub/simplexity/config.ini\n"
+     ]
+    }
+   ],
+   "source": [
+    "# --- Setup ---\n",
+    "import os\n",
+    "from simplexity.loaders import ExperimentLoader\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# Configuration\n",
+    "run_id = \"138baafac5a34726ab070bb1be72a83d\"\n",
+    "tracking_uri = \"databricks\"\n",
+    "config_path = os.path.join('..', 'config.ini') if os.path.basename(os.getcwd()) == 'notebooks' else 'config.ini'\n",
+    "\n",
+    "print(f\"🔍 Run ID: {run_id}\")\n",
+    "print(f\"📍 Tracking URI: {tracking_uri}\")\n",
+    "print(f\"🔧 Config: {os.path.abspath(config_path)}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "✅ ExperimentLoader initialized\n"
+     ]
+    }
+   ],
+   "source": [
+    "# --- Initialize ExperimentLoader ---\n",
+    "loader = ExperimentLoader.from_mlflow(\n",
+    "    run_id=run_id,\n",
+    "    tracking_uri=tracking_uri,\n",
+    "    config_path=config_path\n",
+    ")\n",
+    "print(\"✅ ExperimentLoader initialized\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "📋 Loading experiment configuration...\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "27d3e7414bc24505a5861aa1a49d65b5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Downloading artifacts:   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "🎯 Experiment: example_basic_mess3\n",
+      "🏃 Run: 2025-09-09_22-09-10_example_basic_mess3_42\n",
+      "🤖 Model: transformer_lens.HookedTransformer\n",
+      "💾 Persistence: True\n",
+      "📦 Persister: simplexity.persistence.s3_persister.S3Persister.from_config\n"
+     ]
+    }
+   ],
+   "source": [
+    "# --- 1. Load Configuration ---\n",
+    "print(\"📋 Loading experiment configuration...\")\n",
+    "config = loader.load_config()\n",
+    "\n",
+    "print(f\"🎯 Experiment: {config.experiment_name}\")\n",
+    "print(f\"🏃 Run: {config.run_name}\")\n",
+    "print(f\"🤖 Model: {config.predictive_model.instance._target_}\")\n",
+    "print(f\"💾 Persistence: {bool(config.persistence)}\")\n",
+    "\n",
+    "if config.persistence:\n",
+    "    print(f\"📦 Persister: {config.persistence.instance._target_}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "📊 Loading metrics...\n",
+      "📈 Total metrics: 110,102 entries\n",
+      "🎯 Validation metrics: 102 entries\n",
+      "\n",
+      "📋 Available metrics (13 types):\n",
+      "   • avg_tokens_per_second: 10000 points\n",
+      "   • grad_norm: 10000 points\n",
+      "   • learning_rate: 10000 points\n",
+      "   • loss: 10000 points\n",
+      "   • max_memory_allocated_gb: 10000 points\n",
+      "   • memory_allocated_gb: 10000 points\n",
+      "   • memory_reserved_gb: 10000 points\n",
+      "   • step_time_seconds: 10000 points\n",
+      "   ... and 5 more\n"
+     ]
+    }
+   ],
+   "source": [
+    "# --- 2. Load Metrics ---\n",
+    "print(\"📊 Loading metrics...\")\n",
+    "\n",
+    "# All metrics\n",
+    "df_all = loader.load_metrics()\n",
+    "print(f\"📈 Total metrics: {len(df_all):,} entries\")\n",
+    "\n",
+    "# Validation metrics only\n",
+    "df_val = loader.load_metrics(pattern=\"validation/*\")\n",
+    "print(f\"🎯 Validation metrics: {len(df_val):,} entries\")\n",
+    "\n",
+    "# Show available metric types\n",
+    "metric_types = sorted(df_all['metric'].unique())\n",
+    "print(f\"\\n📋 Available metrics ({len(metric_types)} types):\")\n",
+    "for metric in metric_types[:8]:  # Show first 8\n",
+    "    count = len(df_all[df_all['metric'] == metric])\n",
+    "    print(f\"   • {metric}: {count} points\")\n",
+    "if len(metric_types) > 8:\n",
+    "    print(f\"   ... and {len(metric_types) - 8} more\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "🔍 Discovering checkpoints...\n",
+      "📂 Found 21 checkpoints\n",
+      "   Range: 0 → 10000\n",
+      "   Latest: 10000\n",
+      "   All: [0, 500, 1000, 1500, 2000, 2500, 3000, 3500, 4000, 4500, 5000, 5500, 6000, 6500, 7000, 7500, 8000, 8500, 9000, 9500, 10000]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# --- 3. Discover Checkpoints ---\n",
+    "print(\"🔍 Discovering checkpoints...\")\n",
+    "\n",
+    "checkpoints = loader.list_checkpoints()\n",
+    "latest = loader.latest_checkpoint()\n",
+    "\n",
+    "print(f\"📂 Found {len(checkpoints)} checkpoints\")\n",
+    "if checkpoints:\n",
+    "    print(f\"   Range: {min(checkpoints)} → {max(checkpoints)}\")\n",
+    "    print(f\"   Latest: {latest}\")\n",
+    "    print(f\"   All: {checkpoints}\")\n",
+    "else:\n",
+    "    print(\"   ❌ No checkpoints found (S3 access may be required)\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "🤖 Loading trained model...\n",
+      "✅ Model loaded successfully!\n",
+      "   Type: HookedTransformer\n",
+      "   Parameters: 100,996\n",
+      "   Device: mps:0\n"
+     ]
+    }
+   ],
+   "source": [
+    "# --- 4. Load Model ---\n",
+    "print(\"🤖 Loading trained model...\")\n",
+    "\n",
+    "try:\n",
+    "    model = loader.load_model(step=\"latest\")\n",
+    "    print(f\"✅ Model loaded successfully!\")\n",
+    "    print(f\"   Type: {type(model).__name__}\")\n",
+    "    \n",
+    "    # Get model info\n",
+    "    try:\n",
+    "        param_count = sum(p.numel() for p in model.parameters())\n",
+    "        device = next(model.parameters()).device\n",
+    "        print(f\"   Parameters: {param_count:,}\")\n",
+    "        print(f\"   Device: {device}\")\n",
+    "    except Exception:\n",
+    "        print(f\"   Model details: [unable to inspect]\")\n",
+    "        \n",
+    "except Exception as e:\n",
+    "    print(f\"❌ Could not load model: {e}\")\n",
+    "    if \"transformer_lens\" in str(e):\n",
+    "        print(\"💡 Install missing dependencies: uv add transformer-lens\")\n",
+    "    elif \"checkpoints\" in str(e).lower():\n",
+    "        print(\"💡 Ensure S3 access is configured in config.ini\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "📈 Plotting validation metrics...\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x400 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# --- 5. Visualize Training Progress ---\n",
+    "if not df_val.empty:\n",
+    "    print(\"📈 Plotting validation metrics...\")\n",
+    "    \n",
+    "    val_metrics = sorted(df_val['metric'].unique())\n",
+    "    n_metrics = min(len(val_metrics), 3)  # Show up to 3 metrics\n",
+    "    \n",
+    "    fig, axes = plt.subplots(1, n_metrics, figsize=(5*n_metrics, 4))\n",
+    "    if n_metrics == 1:\n",
+    "        axes = [axes]\n",
+    "    \n",
+    "    for i, metric in enumerate(val_metrics[:n_metrics]):\n",
+    "        data = df_val[df_val['metric'] == metric].sort_values('step')\n",
+    "        axes[i].plot(data['step'], data['value'], 'b-', linewidth=2)\n",
+    "        axes[i].set_title(metric.replace('validation/', ''), fontsize=12)\n",
+    "        axes[i].set_xlabel('Training Step')\n",
+    "        axes[i].set_ylabel('Value')\n",
+    "        axes[i].grid(True, alpha=0.3)\n",
+    "        \n",
+    "        # Add final value annotation\n",
+    "        if len(data) > 0:\n",
+    "            final_val = data['value'].iloc[-1]\n",
+    "            final_step = data['step'].iloc[-1]\n",
+    "            axes[i].annotate(f'{final_val:.3f}', \n",
+    "                           xy=(final_step, final_val),\n",
+    "                           xytext=(10, 10), textcoords='offset points',\n",
+    "                           bbox=dict(boxstyle='round,pad=0.3', facecolor='white', alpha=0.8))\n",
+    "    \n",
+    "    plt.tight_layout()\n",
+    "    plt.show()\n",
+    "else:\n",
+    "    print(\"📈 No validation metrics available for plotting\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Summary\n",
+    "\n",
+    "The `ExperimentLoader` provides a unified interface for loading MLflow experiments:\n",
+    "\n",
+    "### ✅ Core Features\n",
+    "- **Configuration**: Reconstructs complete Hydra configs from MLflow artifacts\n",
+    "- **Metrics**: Loads training/validation metrics with pattern filtering\n",
+    "- **Checkpoints**: Discovers available model checkpoints from S3\n",
+    "- **Models**: Reloads trained models with proper device handling\n",
+    "\n",
+    "### 🔧 Key Methods\n",
+    "- `ExperimentLoader.from_mlflow(run_id, tracking_uri, config_path)`\n",
+    "- `loader.load_config()` - Get experiment configuration\n",
+    "- `loader.load_metrics(pattern=...)` - Load metrics with optional filtering\n",
+    "- `loader.list_checkpoints()` - Discover available checkpoints\n",
+    "- `loader.load_model(step=...)` - Load trained model\n",
+    "\n",
+    "### 📋 Requirements\n",
+    "- MLflow access (Databricks credentials)\n",
+    "- S3 access (AWS credentials + config.ini) for checkpoints\n",
+    "- Model dependencies (e.g., transformer-lens) for model loading"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/pyproject.toml b/pyproject.toml
index aa48833b..78a5b8c1 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -27,6 +27,7 @@ dependencies = [
     "pandas",
     "penzai",
     "plotly",
+    "transformer-lens>=2.16.1",
     "treescope",
 ]
 
diff --git a/simplexity/loaders/__init__.py b/simplexity/loaders/__init__.py
new file mode 100644
index 00000000..9168f1fa
--- /dev/null
+++ b/simplexity/loaders/__init__.py
@@ -0,0 +1,4 @@
+from .experiment_loader import ExperimentLoader
+
+__all__ = ["ExperimentLoader"]
+
diff --git a/simplexity/loaders/experiment_loader.py b/simplexity/loaders/experiment_loader.py
new file mode 100644
index 00000000..ae6723a4
--- /dev/null
+++ b/simplexity/loaders/experiment_loader.py
@@ -0,0 +1,162 @@
+from __future__ import annotations
+
+from dataclasses import dataclass
+from typing import Literal
+
+from omegaconf import DictConfig, OmegaConf
+
+from simplexity.logging.mlflow_reader import MLflowRunReader
+from simplexity.logging.run_reader import RunReader
+from simplexity.persistence.model_persister import ModelPersister
+from simplexity.predictive_models.predictive_model import PredictiveModel
+from simplexity.utils.hydra import typed_instantiate
+
+
+@dataclass
+class ExperimentLoader:
+    """High-level loader for reconstructing models and reading run data."""
+
+    reader: RunReader
+    config_path: str | None = None
+    _cached_config: DictConfig | None = None
+
+    # --- Constructors ---
+    @classmethod
+    def from_mlflow(cls, run_id: str, tracking_uri: str | None = None, config_path: str | None = None) -> "ExperimentLoader":
+        reader = MLflowRunReader(run_id=run_id, tracking_uri=tracking_uri)
+        return cls(reader=reader, config_path=config_path)
+
+    # --- Accessors ---
+    def load_config(self) -> DictConfig:
+        if self._cached_config is None:
+            self._cached_config = self.reader.get_config()
+        return self._cached_config
+
+    def load_metrics(self, pattern: str | None = None):
+        return self.reader.get_metrics(pattern=pattern)
+
+    # --- Helper methods ---
+    def _resolve_device(self, device: str) -> str:
+        """Resolve 'auto' device to actual PyTorch device."""
+        if device != "auto":
+            return device
+        
+        try:
+            import torch
+            if torch.cuda.is_available():
+                return "cuda"
+            elif hasattr(torch.backends, 'mps') and torch.backends.mps.is_available():
+                return "mps"  # Apple Silicon
+            else:
+                return "cpu"
+        except ImportError:
+            return "cpu"
+
+    # --- Model reconstruction ---
+    def _instantiate_model_and_persister(self) -> tuple[object, ModelPersister | None, DictConfig]:
+        cfg = self.load_config()
+        try:
+            # Handle device resolution for 'auto' setting
+            model_config = OmegaConf.to_container(cfg.predictive_model.instance, resolve=False)  # type: ignore[arg-type]
+            if isinstance(model_config, dict):
+                cfg_section = model_config.get('cfg')
+                if isinstance(cfg_section, dict) and cfg_section.get('device') == 'auto':
+                    cfg_section = dict(cfg_section)
+                    cfg_section['device'] = self._resolve_device('auto')
+                    model_config = dict(model_config)
+                    model_config['cfg'] = cfg_section
+            
+            # First, try JAX-style PredictiveModel
+            try:
+                model = typed_instantiate(model_config, PredictiveModel)
+            except Exception:
+                # Fallback: try PyTorch if available
+                try:
+                    import torch  # type: ignore
+                except Exception as torch_import_err:  # pragma: no cover - import guard
+                    raise RuntimeError(
+                        "Failed to instantiate predictive model from run config.\n"
+                        "Tried PredictiveModel (JAX) and PyTorch fallback, but PyTorch is not available.\n"
+                        "Install torch support (uv sync --extra pytorch) or ensure the model package is installed.\n"
+                        f"Underlying error: {torch_import_err}"
+                    ) from torch_import_err
+
+                # Instantiate expecting a torch.nn.Module
+                try:
+                    model = typed_instantiate(model_config, torch.nn.Module)  # type: ignore[attr-defined]
+                    # Put model into eval mode by default for inference
+                    if hasattr(model, "eval"):
+                        model.eval()
+                except Exception as e2:
+                    raise RuntimeError(
+                        "Failed to instantiate predictive model from run config.\n"
+                        "Ensure the model's Python package is installed (e.g., transformer_lens for HookedTransformer).\n"
+                        f"Underlying error: {e2}"
+                    ) from e2
+        except Exception as e:
+            raise RuntimeError(
+                "Failed to instantiate predictive model from run config.\n"
+                "Ensure the model's Python package is installed (e.g., `transformer_lens`).\n"
+                f"Underlying error: {e}"
+            ) from e
+        persister: ModelPersister | None
+        if cfg.persistence:
+            try:
+                # Override config_filename if custom config_path is provided
+                persister_config = OmegaConf.to_container(cfg.persistence.instance, resolve=False)  # type: ignore[arg-type]
+                if isinstance(persister_config, dict) and self.config_path:
+                    if 'config_filename' in persister_config:
+                        persister_config = dict(persister_config)
+                        persister_config['config_filename'] = self.config_path
+                persister = typed_instantiate(persister_config, ModelPersister)
+            except Exception as e:
+                raise RuntimeError(
+                    "Failed to instantiate persister from run config.\n"
+                    "If using S3, ensure credentials/config are available (e.g., config.ini or env).\n"
+                    f"Underlying error: {e}"
+                ) from e
+        else:
+            persister = None
+        return model, persister, cfg
+
+    def _instantiate_persister_only(self) -> ModelPersister | None:
+        cfg = self.load_config()
+        if not cfg.persistence:
+            return None
+        try:
+            # Override config_filename if custom config_path is provided
+            persister_config = OmegaConf.to_container(cfg.persistence.instance, resolve=False)  # type: ignore[arg-type]
+            if isinstance(persister_config, dict) and self.config_path:
+                if 'config_filename' in persister_config:
+                    persister_config = dict(persister_config)
+                    persister_config['config_filename'] = self.config_path
+            return typed_instantiate(persister_config, ModelPersister)
+        except Exception:
+            # Best-effort: return None if we cannot construct the persister (missing creds, etc.)
+            return None
+
+    def list_checkpoints(self) -> list[int]:
+        persister = self._instantiate_persister_only()
+        return persister.list_checkpoints() if persister else []
+
+    def latest_checkpoint(self) -> int | None:
+        persister = self._instantiate_persister_only()
+        return persister.latest_checkpoint() if persister else None
+
+    def load_model(self, step: int | Literal["latest"] = "latest") -> object:
+        model, persister, _ = self._instantiate_model_and_persister()
+        if not persister:
+            raise RuntimeError("No persistence configuration found in run config; cannot load checkpoints.")
+
+        target_step: int
+        if step == "latest":
+            latest = persister.latest_checkpoint()
+            if latest is None:
+                raise RuntimeError("No checkpoints found for this run.")
+            target_step = latest
+        else:
+            if not persister.checkpoint_exists(step):
+                raise RuntimeError(f"Requested checkpoint step {step} does not exist.")
+            target_step = step
+
+        return persister.load_weights(model, target_step)
diff --git a/simplexity/logging/mlflow_reader.py b/simplexity/logging/mlflow_reader.py
new file mode 100644
index 00000000..93d376b6
--- /dev/null
+++ b/simplexity/logging/mlflow_reader.py
@@ -0,0 +1,103 @@
+from __future__ import annotations
+
+import fnmatch
+import tempfile
+from pathlib import Path
+from typing import Iterable
+
+import mlflow
+import pandas as pd
+from mlflow.entities import Metric
+from omegaconf import DictConfig, OmegaConf
+
+from simplexity.logging.run_reader import RunReader
+
+
+class MLflowRunReader(RunReader):
+    """Read experiment data for a single MLflow run."""
+
+    def __init__(self, run_id: str, tracking_uri: str | None = None) -> None:
+        self._run_id = run_id
+        # The global mlflow.set_tracking_uri also works, but we keep it local
+        self._client = mlflow.MlflowClient(tracking_uri=tracking_uri)
+        self._temp_dir = tempfile.TemporaryDirectory()
+
+    # --- Basic run info helpers ---
+    def _get_run(self):
+        return self._client.get_run(self._run_id)
+
+    # --- Config/params/tags ---
+    def get_config(self) -> DictConfig:
+        # We expect the config artifact to be saved as "config.yaml" at the root.
+        # If not found, search the artifact tree for a file named config.yaml.
+        dst_dir = Path(self._temp_dir.name) / "config"
+        dst_dir.mkdir(parents=True, exist_ok=True)
+        try:
+            local_path = self._client.download_artifacts(self._run_id, "config.yaml", str(dst_dir))
+            return OmegaConf.load(local_path)
+        except Exception:
+            for path in self.list_artifacts():
+                if Path(path).name == "config.yaml":
+                    local_path = self._client.download_artifacts(self._run_id, path, str(dst_dir))
+                    return OmegaConf.load(local_path)
+            raise RuntimeError(
+                "Could not find config.yaml in MLflow artifacts for this run."
+            )
+
+    def get_params(self) -> dict[str, str]:
+        run = self._get_run()
+        # mlflow returns a dict-like mapping of strings
+        return dict(run.data.params)
+
+    def get_tags(self) -> dict[str, str]:
+        run = self._get_run()
+        return dict(run.data.tags)
+
+    # --- Metrics ---
+    def get_metrics(self, pattern: str | None = None) -> pd.DataFrame:
+        run = self._get_run()
+        metric_keys = list(run.data.metrics.keys())
+        if pattern:
+            # Support glob-style filtering (e.g., "validation/*")
+            metric_keys = [k for k in metric_keys if fnmatch.fnmatch(k, pattern)]
+
+        records: list[tuple[str, int, float, int]] = []
+        for key in metric_keys:
+            history: list[Metric] = self._client.get_metric_history(self._run_id, key)
+            for m in history:
+                records.append((key, int(m.step), float(m.value), int(m.timestamp)))
+        if not records:
+            return pd.DataFrame(columns=["metric", "step", "value", "timestamp"])
+        df = pd.DataFrame.from_records(records, columns=["metric", "step", "value", "timestamp"])
+        df.sort_values(["metric", "step"], inplace=True)
+        return df
+
+    # --- Artifacts ---
+    def list_artifacts(self, path: str | None = None) -> list[str]:
+        """List artifact relative paths (recursively)."""
+        base = path or ""
+        results: list[str] = []
+
+        def _recurse(rel: str) -> None:
+            for e in self._client.list_artifacts(self._run_id, rel):
+                child = f"{rel}/{e.path}" if rel else e.path
+                if e.is_dir:
+                    _recurse(child)
+                else:
+                    results.append(child)
+
+        _recurse(base)
+        return results
+
+    def download_artifact(self, path: str, dst: str | Path | None = None) -> Path:
+        dst_dir = Path(dst) if dst is not None else Path(self._temp_dir.name) / "artifacts"
+        dst_dir.mkdir(parents=True, exist_ok=True)
+        local_path = self._client.download_artifacts(self._run_id, path, str(dst_dir))
+        return Path(local_path)
+
+    # --- Cleanup ---
+    def __del__(self) -> None:  # pragma: no cover - best-effort cleanup
+        try:
+            self._temp_dir.cleanup()
+        except Exception:
+            pass
diff --git a/simplexity/logging/run_reader.py b/simplexity/logging/run_reader.py
new file mode 100644
index 00000000..05a65d76
--- /dev/null
+++ b/simplexity/logging/run_reader.py
@@ -0,0 +1,37 @@
+from __future__ import annotations
+
+from abc import ABC, abstractmethod
+from pathlib import Path
+from typing import Protocol
+
+import pandas as pd
+from omegaconf import DictConfig
+
+
+class RunReader(Protocol):
+    """Protocol for reading experiment run data from a tracking backend."""
+
+    def get_config(self) -> DictConfig:
+        """Return the saved run config as a DictConfig."""
+        ...
+
+    def get_params(self) -> dict[str, str]:
+        """Return run parameters as a simple dict of strings."""
+        ...
+
+    def get_tags(self) -> dict[str, str]:
+        """Return run tags as a simple dict of strings."""
+        ...
+
+    def get_metrics(self, pattern: str | None = None) -> pd.DataFrame:
+        """Return metrics as a tidy DataFrame with columns: metric, step, value, timestamp."""
+        ...
+
+    def list_artifacts(self, path: str | None = None) -> list[str]:
+        """List artifact paths stored for this run (relative paths)."""
+        ...
+
+    def download_artifact(self, path: str, dst: str | Path | None = None) -> Path:
+        """Download an artifact relative path to a destination directory and return the local file path."""
+        ...
+
diff --git a/simplexity/persistence/local_equinox_persister.py b/simplexity/persistence/local_equinox_persister.py
index fcde8d7f..acdb862f 100644
--- a/simplexity/persistence/local_equinox_persister.py
+++ b/simplexity/persistence/local_equinox_persister.py
@@ -28,3 +28,30 @@ def load_weights(self, model: PredictiveModel, step: int = 0) -> PredictiveModel
 
     def _get_path(self, step: int) -> Path:
         return self.directory / str(step) / self.filename
+
+    # --- Checkpoint discovery ---
+    def list_checkpoints(self) -> list[int]:
+        steps: list[int] = []
+        if not self.directory.exists():
+            return steps
+        for child in self.directory.iterdir():
+            if child.is_dir():
+                try:
+                    step = int(child.name)
+                except ValueError:
+                    continue
+                if (child / self.filename).exists():
+                    steps.append(step)
+        steps.sort()
+        return steps
+
+    def latest_checkpoint(self) -> int | None:
+        steps = self.list_checkpoints()
+        return steps[-1] if steps else None
+
+    def checkpoint_exists(self, step: int) -> bool:
+        return (self.directory / str(step) / self.filename).exists()
+
+    def uri_for_step(self, step: int) -> str:
+        path = self._get_path(step)
+        return f"file://{path}"
diff --git a/simplexity/persistence/local_penzai_persister.py b/simplexity/persistence/local_penzai_persister.py
index ac3a6449..97da6525 100644
--- a/simplexity/persistence/local_penzai_persister.py
+++ b/simplexity/persistence/local_penzai_persister.py
@@ -37,3 +37,22 @@ def load_weights(self, model: PredictiveModel, step: int = 0) -> PredictiveModel
         unbound_model, _ = pz.unbind_variables(model)
         variable_values = reconstruct_variables(items)
         return pz.bind_variables(unbound_model, variable_values)
+
+    # --- Checkpoint discovery ---
+    def list_checkpoints(self) -> list[int]:
+        mngr = ocp.CheckpointManager(self.directory, handler_registry=self.registry)
+        steps = list(mngr.all_steps())
+        steps.sort()
+        return steps
+
+    def latest_checkpoint(self) -> int | None:
+        steps = self.list_checkpoints()
+        return steps[-1] if steps else None
+
+    def checkpoint_exists(self, step: int) -> bool:
+        return step in set(self.list_checkpoints())
+
+    def uri_for_step(self, step: int) -> str:
+        # Orbax uses a directory per checkpoint step
+        path = self.directory / str(step)
+        return f"file://{path}"
diff --git a/simplexity/persistence/local_pytorch_persister.py b/simplexity/persistence/local_pytorch_persister.py
index 7696406d..20d08f40 100644
--- a/simplexity/persistence/local_pytorch_persister.py
+++ b/simplexity/persistence/local_pytorch_persister.py
@@ -39,3 +39,30 @@ def load_weights(self, model: torch.nn.Module, step: int = 0) -> torch.nn.Module
 
     def _get_path(self, step: int) -> Path:
         return self.directory / str(step) / self.filename
+
+    # --- Checkpoint discovery ---
+    def list_checkpoints(self) -> list[int]:
+        steps: list[int] = []
+        if not self.directory.exists():
+            return steps
+        for child in self.directory.iterdir():
+            if child.is_dir():
+                try:
+                    step = int(child.name)
+                except ValueError:
+                    continue
+                if (child / self.filename).exists():
+                    steps.append(step)
+        steps.sort()
+        return steps
+
+    def latest_checkpoint(self) -> int | None:
+        steps = self.list_checkpoints()
+        return steps[-1] if steps else None
+
+    def checkpoint_exists(self, step: int) -> bool:
+        return (self.directory / str(step) / self.filename).exists()
+
+    def uri_for_step(self, step: int) -> str:
+        path = self._get_path(step)
+        return f"file://{path}"
diff --git a/simplexity/persistence/model_persister.py b/simplexity/persistence/model_persister.py
index 493c9fd6..1e457630 100644
--- a/simplexity/persistence/model_persister.py
+++ b/simplexity/persistence/model_persister.py
@@ -28,3 +28,27 @@ def save_weights(self, model: PredictiveModel, step: int = 0) -> None:
     def load_weights(self, model: PredictiveModel, step: int = 0) -> PredictiveModel:
         """Load weights into an existing model instance."""
         ...
+
+    # --- Checkpoint discovery helpers ---
+    @abstractmethod
+    def list_checkpoints(self) -> list[int]:
+        """List all available checkpoint steps in this persister."""
+        ...
+
+    @abstractmethod
+    def latest_checkpoint(self) -> int | None:
+        """Return the latest checkpoint step, or None if none exist."""
+        ...
+
+    @abstractmethod
+    def checkpoint_exists(self, step: int) -> bool:
+        """Return True if a checkpoint exists for the given step."""
+        ...
+
+    def uri_for_step(self, step: int) -> str:
+        """Return a URI for the checkpoint at the given step.
+
+        Subclasses should override this to provide a stable artifact URI
+        (e.g., file://, s3://). Default implementation returns an empty string.
+        """
+        return ""
diff --git a/simplexity/persistence/s3_persister.py b/simplexity/persistence/s3_persister.py
index 2ff32458..1a6aa34f 100644
--- a/simplexity/persistence/s3_persister.py
+++ b/simplexity/persistence/s3_persister.py
@@ -161,3 +161,45 @@ def _download_s3_object(self, object_name: str, file_name: str) -> None:
                 raise RuntimeError(f"Failed to load {file_name} from S3: {e}") from e
         except Exception as e:
             raise RuntimeError(f"Unexpected error loading {file_name} from S3: {e}") from e
+
+    # --- Checkpoint discovery ---
+    def list_checkpoints(self) -> list[int]:
+        """List available checkpoint steps under the configured prefix.
+
+        We infer steps as the first path component under `prefix`.
+        """
+        paginator = self.s3_client.get_paginator("list_objects_v2")
+        steps_set: set[int] = set()
+        prefix = f"{self.prefix}/"
+        for page in paginator.paginate(Bucket=self.bucket, Prefix=prefix):
+            for obj in page.get("Contents", []):
+                key = obj.get("Key", "")
+                try:
+                    rel = Path(key).relative_to(self.prefix)
+                except Exception:
+                    # If key equals prefix with no trailing component, skip
+                    continue
+                # rel like '123/model.eqx' or '123/...' -> first part is step
+                parts = rel.parts
+                if parts:
+                    try:
+                        step = int(parts[0])
+                        steps_set.add(step)
+                    except ValueError:
+                        continue
+        steps = sorted(steps_set)
+        return steps
+
+    def latest_checkpoint(self) -> int | None:
+        steps = self.list_checkpoints()
+        return steps[-1] if steps else None
+
+    def checkpoint_exists(self, step: int) -> bool:
+        return step in set(self.list_checkpoints())
+
+    def uri_for_step(self, step: int) -> str:
+        # Best-effort: point to directory or file depending on local persister
+        filename = getattr(self.local_persister, "filename", None)
+        if filename:
+            return f"s3://{self.bucket}/{self.prefix}/{step}/{filename}"
+        return f"s3://{self.bucket}/{self.prefix}/{step}"
diff --git a/tests/loaders/test_experiment_loader.py b/tests/loaders/test_experiment_loader.py
new file mode 100644
index 00000000..7d6a3b0d
--- /dev/null
+++ b/tests/loaders/test_experiment_loader.py
@@ -0,0 +1,67 @@
+from __future__ import annotations
+
+from pathlib import Path
+from types import SimpleNamespace
+
+import pandas as pd
+import pytest
+from omegaconf import OmegaConf
+
+from simplexity.loaders.experiment_loader import ExperimentLoader
+from simplexity.logging.run_reader import RunReader
+
+
+class _StubReader(RunReader):
+    def __init__(self, cfg):
+        self._cfg = cfg
+
+    def get_config(self):
+        return self._cfg
+
+    def get_params(self):  # pragma: no cover - not used
+        return {}
+
+    def get_tags(self):  # pragma: no cover - not used
+        return {}
+
+    def get_metrics(self, pattern: str | None = None):  # pragma: no cover - not used
+        return pd.DataFrame(columns=["metric", "step", "value", "timestamp"])
+
+    def list_artifacts(self, path: str | None = None):  # pragma: no cover - not used
+        return []
+
+    def download_artifact(self, path: str, dst: str | Path | None = None):  # pragma: no cover - not used
+        raise NotImplementedError
+
+
+def test_list_checkpoints_without_model_instantiation(tmp_path: Path):
+    # Prepare a config that would fail if model instantiation is attempted
+    cfg_dict = {
+        "predictive_model": {
+            "name": "broken",
+            "instance": {
+                "_target_": "nonexistent.module.Class",  # Must not be imported in this test
+            },
+        },
+        "persistence": {
+            "name": "local_equinox_persister",
+            "instance": {
+                "_target_": "simplexity.persistence.local_equinox_persister.LocalEquinoxPersister",
+                "directory": str(tmp_path),
+                "filename": "model.eqx",
+            },
+        },
+    }
+    cfg = OmegaConf.create(cfg_dict)
+
+    # Create fake checkpoints
+    (tmp_path / "0").mkdir(parents=True, exist_ok=True)
+    (tmp_path / "0" / "model.eqx").write_bytes(b"")
+    (tmp_path / "3").mkdir(parents=True, exist_ok=True)
+    (tmp_path / "3" / "model.eqx").write_bytes(b"")
+
+    loader = ExperimentLoader(reader=_StubReader(cfg))
+    steps = loader.list_checkpoints()
+    assert steps == [0, 3]
+    assert loader.latest_checkpoint() == 3
+
diff --git a/tests/logging/test_mlflow_reader.py b/tests/logging/test_mlflow_reader.py
new file mode 100644
index 00000000..d50fc4e2
--- /dev/null
+++ b/tests/logging/test_mlflow_reader.py
@@ -0,0 +1,73 @@
+from __future__ import annotations
+
+import os
+from pathlib import Path
+from types import SimpleNamespace
+from unittest.mock import MagicMock, patch
+
+import pandas as pd
+import pytest
+
+from simplexity.logging.mlflow_reader import MLflowRunReader
+
+
+class _Entry:
+    def __init__(self, path: str, is_dir: bool) -> None:
+        self.path = path
+        self.is_dir = is_dir
+
+
+def test_mlflow_reader_metrics_and_config(tmp_path: Path):
+    """Basic test of metrics DataFrame and config fallback search."""
+    with patch("mlflow.MlflowClient") as mock_client_cls:
+        mock_client = MagicMock()
+        mock_client_cls.return_value = mock_client
+
+        # Mock run with metric keys
+        run = SimpleNamespace(
+            data=SimpleNamespace(metrics={"loss": 0.1, "validation/loss": 0.2}, params={}, tags={})
+        )
+        mock_client.get_run.return_value = run
+
+        # Metric history (list of objects with .step/.value/.timestamp)
+        class M:
+            def __init__(self, step, value, ts):
+                self.step = step
+                self.value = value
+                self.timestamp = ts
+
+        mock_client.get_metric_history.side_effect = lambda run_id, key: [M(0, 1.0, 1), M(1, 0.9, 2)]
+
+        # list_artifacts recursive: "" -> [nested/], "nested" -> [config.yaml]
+        def list_artifacts(run_id, rel):
+            return [_Entry("nested", True)] if rel == "" else [_Entry("config.yaml", False)]
+
+        mock_client.list_artifacts.side_effect = list_artifacts
+
+        # download_artifacts: fail for root config, succeed for nested/config.yaml
+        def download_artifacts(run_id, path, dst):
+            out_dir = Path(dst)
+            out_dir.mkdir(parents=True, exist_ok=True)
+            if path == "config.yaml":
+                raise RuntimeError("not at root")
+            # nested/config.yaml
+            local = out_dir / "config.yaml"
+            local.write_text(
+                "predictive_model:\n  name: test\n  instance:\n    _target_: simplexity.predictive_models.gru_rnn.build_gru_rnn\n    vocab_size: 2\n\n# minimal persistence section to avoid None-handling paths\npersistence: null\n"
+            )
+            return str(local)
+
+        mock_client.download_artifacts.side_effect = download_artifacts
+
+        reader = MLflowRunReader(run_id="abc", tracking_uri=os.environ.get("MLFLOW_TRACKING_URI"))
+
+        # Metrics
+        df = reader.get_metrics()
+        assert isinstance(df, pd.DataFrame)
+        assert set(df.columns) == {"metric", "step", "value", "timestamp"}
+        assert set(df["metric"].unique()) == {"loss", "validation/loss"}
+
+        # Config
+        cfg = reader.get_config()
+        assert cfg.predictive_model.name == "test"
+
diff --git a/tests/persistence/test_checkpoint_discovery_local.py b/tests/persistence/test_checkpoint_discovery_local.py
new file mode 100644
index 00000000..6a4f98e5
--- /dev/null
+++ b/tests/persistence/test_checkpoint_discovery_local.py
@@ -0,0 +1,91 @@
+from pathlib import Path
+
+import jax
+import pytest
+
+from simplexity.persistence.local_equinox_persister import LocalEquinoxPersister
+from simplexity.persistence.local_penzai_persister import LocalPenzaiPersister
+from simplexity.persistence.local_pytorch_persister import LocalPytorchPersister
+from simplexity.predictive_models.gru_rnn import GRURNN
+
+
+def get_eqx_model(seed: int = 0) -> GRURNN:
+    return GRURNN(vocab_size=2, embedding_size=4, hidden_sizes=[3, 3], key=jax.random.PRNGKey(seed))
+
+
+def test_local_equinox_checkpoint_discovery(tmp_path: Path):
+    persister = LocalEquinoxPersister(tmp_path, filename="model.eqx")
+    model = get_eqx_model(0)
+
+    # No checkpoints initially
+    assert persister.list_checkpoints() == []
+    assert persister.latest_checkpoint() is None
+    assert not persister.checkpoint_exists(0)
+
+    # Save a couple checkpoints
+    persister.save_weights(model, 0)
+    persister.save_weights(model, 5)
+
+    assert persister.list_checkpoints() == [0, 5]
+    assert persister.latest_checkpoint() == 5
+    assert persister.checkpoint_exists(0)
+    assert not persister.checkpoint_exists(1)
+    assert persister.uri_for_step(5).startswith("file://")
+
+
+def test_local_pytorch_checkpoint_discovery(tmp_path: Path):
+    try:
+        import torch  # type: ignore
+    except Exception:
+        pytest.skip("PyTorch not available")
+
+    class Simple(torch.nn.Module):  # type: ignore
+        def __init__(self):  # pragma: no cover - simple model definition
+            super().__init__()
+            self.l = torch.nn.Linear(2, 2)
+
+        def forward(self, x):  # pragma: no cover - not used
+            return self.l(x)
+
+    persister = LocalPytorchPersister(tmp_path, filename="model.pt")
+    model = Simple()
+
+    assert persister.list_checkpoints() == []
+    persister.save_weights(model, 0)
+    persister.save_weights(model, 2)
+    assert persister.list_checkpoints() == [0, 2]
+    assert persister.latest_checkpoint() == 2
+    assert persister.checkpoint_exists(2)
+    assert persister.uri_for_step(2).startswith("file://")
+
+
+def test_local_penzai_checkpoint_discovery(tmp_path: Path):
+    try:
+        from penzai.models.transformer.variants.llamalike_common import (
+            LlamalikeTransformerConfig,
+            build_llamalike_transformer,
+        )
+    except Exception:
+        pytest.skip("Penzai not available")
+
+    config = LlamalikeTransformerConfig(
+        num_kv_heads=1,
+        query_head_multiplier=1,
+        embedding_dim=16,
+        projection_dim=16,
+        mlp_hidden_dim=16,
+        num_decoder_blocks=1,
+        vocab_size=8,
+        mlp_variant="geglu_approx",
+        tie_embedder_and_logits=False,
+    )
+    model = build_llamalike_transformer(config, init_base_rng=jax.random.PRNGKey(0))
+
+    persister = LocalPenzaiPersister(tmp_path)
+    assert persister.list_checkpoints() == []
+    persister.save_weights(model, 3)
+    persister.save_weights(model, 7)
+    assert persister.list_checkpoints() == [3, 7]
+    assert persister.latest_checkpoint() == 7
+    assert persister.checkpoint_exists(3)
+    assert persister.uri_for_step(7).startswith("file://")
diff --git a/tests/persistence/test_checkpoint_discovery_s3.py b/tests/persistence/test_checkpoint_discovery_s3.py
new file mode 100644
index 00000000..b6b8bf88
--- /dev/null
+++ b/tests/persistence/test_checkpoint_discovery_s3.py
@@ -0,0 +1,41 @@
+import tempfile
+from pathlib import Path
+
+import jax
+
+from simplexity.persistence.local_equinox_persister import LocalEquinoxPersister
+from simplexity.persistence.s3_persister import S3Persister
+from simplexity.predictive_models.gru_rnn import GRURNN
+from tests.persistence.s3_mocks import MockS3Client
+
+
+def get_model(seed: int = 0) -> GRURNN:
+    return GRURNN(vocab_size=2, embedding_size=4, hidden_sizes=[3, 3], key=jax.random.PRNGKey(seed))
+
+
+def test_s3_checkpoint_discovery(tmp_path: Path):
+    s3_client = MockS3Client(tmp_path)
+    temp_dir = tempfile.TemporaryDirectory()
+    local = LocalEquinoxPersister(temp_dir.name)
+    persister = S3Persister(
+        bucket="b",
+        prefix="p",
+        s3_client=s3_client,
+        temp_dir=temp_dir,
+        local_persister=local,
+    )
+
+    # No checkpoints initially
+    assert persister.list_checkpoints() == []
+    assert persister.latest_checkpoint() is None
+
+    model = get_model(0)
+    persister.save_weights(model, 0)
+    persister.save_weights(model, 7)
+
+    assert persister.list_checkpoints() == [0, 7]
+    assert persister.latest_checkpoint() == 7
+    assert persister.checkpoint_exists(0)
+    # URI should include filename when available
+    assert persister.uri_for_step(7) == "s3://b/p/7/model.eqx"
+
diff --git a/uv.lock b/uv.lock
index ef2042c4..e00744c4 100644
--- a/uv.lock
+++ b/uv.lock
@@ -1,10 +1,12 @@
 version = 1
-revision = 2
+revision = 3
 requires-python = ">=3.12"
 resolution-markers = [
-    "python_full_version >= '3.13' and sys_platform != 'win32'",
+    "python_full_version >= '3.13' and sys_platform == 'linux'",
+    "python_full_version >= '3.13' and sys_platform != 'linux' and sys_platform != 'win32'",
     "python_version < '0'",
-    "python_full_version < '3.13' and sys_platform != 'win32'",
+    "python_full_version < '3.13' and sys_platform == 'linux'",
+    "python_full_version < '3.13' and sys_platform != 'linux' and sys_platform != 'win32'",
     "python_full_version >= '3.13' and sys_platform == 'win32'",
     "python_full_version < '3.13' and sys_platform == 'win32'",
 ]
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