diff --git a/eval/configs/eval_config.yaml b/eval/configs/eval_config.yaml
new file mode 100644
index 00000000..6cc54c9e
--- /dev/null
+++ b/eval/configs/eval_config.yaml
@@ -0,0 +1,25 @@
+# Model configuration
+model: Qwen/Qwen2.5-0.5B-Instruct  # Change to the smaller model
+hf_path: /workspace/joe/verl_grpo_qwen_3b_curr
+max_tokens: 1024  # From max_response_length in training config
+temperature: 0.7  # Lower temperature for more focused responses
+top_p: 0.9  # From rollout top_p
+developer_prompt: DeepSeekZero
+developer_role: system  # Standard role for system prompts
+
+# Output configuration
+output_dir: eval_results
+save_metadata: true
+save_full_results: true
+
+# Categories and datasets to evaluate
+categories:
+  - category: reasoning
+    datasets:
+      - dataset: spell_backward
+        size: 1000  # From training dataset_size
+        seed: 42
+        params:
+          min_word_len: 3  # From training config
+          max_word_len: 10
+          data_file: holdout_words.txt
diff --git a/eval/local_eval.py b/eval/local_eval.py
new file mode 100644
index 00000000..22fa2d04
--- /dev/null
+++ b/eval/local_eval.py
@@ -0,0 +1,486 @@
+#!/usr/bin/env python
+"""
+Local evaluation script for reasoning gym datasets.
+
+This script evaluates local LLM performance on reasoning gym datasets.
+
+Usage:
+    python local_eval.py --model-path PATH --config config.yaml [options]
+
+Options:
+    --model-path PATH         Path to local model directory/file
+    --config CONFIG          Path to configuration file
+    --output-dir DIR         Override output directory specified in config
+    --category CATEGORY      Evaluate only datasets from this category
+    --device DEVICE         Device to run model on (cuda:0, cpu, etc.)
+    --batch-size N          Batch size for inference
+    --verbose               Print detailed model responses
+    --debug                 Enable debug logging
+"""
+
+import argparse
+import asyncio
+import json
+import logging
+import sys
+from dataclasses import dataclass
+from datetime import datetime
+from pathlib import Path
+from typing import Any, Dict, List, Optional
+
+import torch
+import yaml
+from eval_config import CategoryConfig, DatasetConfig, EvalConfig
+from tqdm import tqdm
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+import reasoning_gym
+from reasoning_gym.utils import SYSTEM_PROMPTS, extract_answer
+
+try:
+    import torch.distributed as dist
+    import torch.distributed._tensor as dt
+
+    HAS_DIST_TENSOR = True
+except ImportError:
+    HAS_DIST_TENSOR = False
+
+import importlib
+from collections import OrderedDict
+
+
+@dataclass
+class DatasetConfig:
+    dataset: str
+    size: Optional[int] = None
+    seed: Optional[int] = None
+    params: Dict[str, Any] = None
+
+    def __post_init__(self):
+        if self.params is None:
+            self.params = {}
+
+
+@dataclass
+class CategoryConfig:
+    category: str
+    datasets: List[DatasetConfig]
+
+    @classmethod
+    def from_dict(cls, data: Dict[str, Any]) -> "CategoryConfig":
+        datasets = [DatasetConfig(**dataset) for dataset in data["datasets"]]
+        return cls(category=data["category"], datasets=datasets)
+
+
+@dataclass
+class EvalConfig:
+    # Model configuration
+    model: str
+    hf_path: Optional[str]
+    max_tokens: int
+    temperature: float
+    top_p: float
+    developer_prompt: str
+    developer_role: str
+
+    # Output configuration
+    output_dir: str
+    save_metadata: bool
+    save_full_results: bool
+
+    # Categories
+    categories: List[CategoryConfig]
+
+    @classmethod
+    def from_yaml(cls, path: str) -> "EvalConfig":
+        with open(path, "r") as f:
+            data = yaml.safe_load(f)
+
+        # Convert categories data to CategoryConfig objects
+        categories = [CategoryConfig.from_dict(cat) for cat in data["categories"]]
+
+        # Remove categories from data dict and pass remaining args to constructor
+        data["categories"] = categories
+
+        return cls(**data)
+
+    def get_system_prompt(self) -> str:
+        """Get the system prompt to use for evaluation."""
+        return SYSTEM_PROMPTS[self.developer_prompt]
+
+
+class LocalModelEvaluator:
+    """Evaluates local models on reasoning datasets."""
+
+    def __init__(
+        self,
+        model_path: str,
+        config: EvalConfig,
+        hf_path: Optional[str] = None,
+        device: str = "cuda:0",
+        batch_size: int = 1,
+        verbose: bool = False,
+        debug: bool = False,
+    ):
+        """Initialize the evaluator.
+
+        Args:
+            model_path: Path to base model or model identifier
+            config: Evaluation configuration
+            hf_path: Path to HuggingFace checkpoint directory containing trained weights
+            device: Device to run model on
+            batch_size: Batch size for inference
+            verbose: Whether to print detailed responses
+            debug: Whether to enable debug logging
+        """
+        self.config = config
+        self.device = device
+        self.batch_size = batch_size
+        self.verbose = verbose
+        self.developer_prompt = config.developer_prompt
+        self.developer_role = config.developer_role
+
+        # Set up logging
+        self.logger = logging.getLogger("LocalModelEvaluator")
+        if debug:
+            self.logger.setLevel(logging.DEBUG)
+
+        # Load base model and tokenizer
+        self.logger.info(f"Loading base model from {model_path}")
+        self.model = AutoModelForCausalLM.from_pretrained(
+            model_path,
+            torch_dtype=torch.bfloat16 if "cuda" in device else torch.float32,
+            device_map=device,
+            trust_remote_code=True,  # Required for Qwen models
+        )
+        self.tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)  # Required for Qwen models
+
+        # Try to load from the huggingface folder if it exists
+        if hf_path:
+            self.logger.info(f"Loading trained weights from {hf_path}")
+
+            # First load the base model to ensure compatibility
+            self.model = AutoModelForCausalLM.from_pretrained(
+                model_path,
+                torch_dtype=torch.bfloat16 if "cuda" in device else torch.float32,
+                device_map=device,
+                trust_remote_code=True,
+            )
+            self.tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
+
+            # Path to distributed checkpoint
+            state_dict_path = Path(hf_path) / "global_step_312/actor/model_world_size_2_rank_0.pt"
+
+            if state_dict_path.exists():
+                self.logger.info(f"Loading distributed state dict from {state_dict_path}")
+
+                # Load the checkpoint with special handling
+                try:
+                    # Custom loading with mapping to CPU first
+                    checkpoint = torch.load(state_dict_path, map_location="cpu")
+
+                    # Create a clean state dict
+                    clean_state_dict = OrderedDict()
+
+                    # Process each key-value pair
+                    for k, v in checkpoint.items():
+                        # Remove the "module." prefix if present
+                        if k.startswith("module."):
+                            k = k[7:]
+
+                        # Handle DTensor objects
+                        if hasattr(v, "_local_tensor"):
+                            # For distributed tensor - extract local tensor
+                            v = v._local_tensor
+                        elif str(type(v)).find("DTensor") > 0:
+                            # Alternative way to detect DTensor
+                            v = v.to_local() if hasattr(v, "to_local") else v
+
+                        # Add to clean state dict
+                        clean_state_dict[k] = v
+
+                    # Load the cleaned state dict
+                    missing, unexpected = self.model.load_state_dict(clean_state_dict, strict=False)
+
+                    self.logger.info(
+                        f"Successfully loaded checkpoint. Missing keys: {len(missing)}, Unexpected keys: {len(unexpected)}"
+                    )
+                    if self.verbose:
+                        self.logger.info(f"Missing keys: {missing[:10]}...")
+                        self.logger.info(f"Unexpected keys: {unexpected[:10]}...")
+
+                except Exception as e:
+                    self.logger.error(f"Error loading distributed checkpoint: {e}")
+                    raise RuntimeError(f"Failed to load distributed checkpoint: {e}")
+            else:
+                raise FileNotFoundError(f"Checkpoint file not found: {state_dict_path}")
+
+        # Move model to device if needed
+        if not hasattr(self.model, "hf_device_map"):
+            self.model.to(device)
+
+        # Metadata
+        self.start_time = datetime.now()
+
+    def get_model_response(self, prompt: str) -> list[str]:
+        """Get response from local model."""
+        if self.verbose:
+            self.logger.info(f"Prompt: {prompt}")
+
+        # Prepare full prompt with system message
+        chat = []
+        if self.developer_prompt is not None:
+            chat.append({"role": self.developer_role, "content": self.config.get_system_prompt()})
+        chat.append({"role": "user", "content": prompt})
+
+        # Use the model's chat template
+        full_prompt = self.tokenizer.apply_chat_template(chat, tokenize=False, add_generation_prompt=True)
+
+        # Tokenize
+        inputs = self.tokenizer(
+            full_prompt, return_tensors="pt", truncation=True, max_length=self.tokenizer.model_max_length
+        ).to(self.device)
+
+        # Generate
+        with torch.no_grad():
+            outputs = self.model.generate(
+                **inputs,
+                max_new_tokens=self.config.max_tokens,
+                temperature=self.config.temperature,
+                top_p=self.config.top_p,
+                do_sample=True if self.config.temperature > 0 else False,
+                num_return_sequences=1,
+                pad_token_id=self.tokenizer.eos_token_id,
+            )
+
+        # Decode only the new tokens
+        response = self.tokenizer.decode(outputs[0][inputs.input_ids.shape[1] :], skip_special_tokens=True).strip()
+
+        if self.verbose:
+            self.logger.info(f"Response: {response}")
+
+        return [response]
+
+    def process_entry(
+        self,
+        dataset: reasoning_gym.dataset.ProceduralDataset,
+        entry: dict[str, Any],
+        entry_index: int,
+        dataset_name: str,
+    ) -> dict[str, Any]:
+        """Process a single dataset entry."""
+        try:
+            # Get model response
+            responses = self.get_model_response(entry["question"])
+            response = responses[0]
+
+            # Extract and score answer
+            model_answer = extract_answer(response)
+
+            if self.verbose:
+                self.logger.info(f"Question: {entry['question']}")
+                self.logger.info(f"Expected: {entry['answer']}")
+                self.logger.info(f"Model answer: {model_answer}")
+                self.logger.info(f"Full response: {response}")
+
+            # Score the answer
+            score = dataset.score_answer(answer=model_answer, entry=entry)
+
+            if self.verbose:
+                self.logger.info(f"Score: {score}")
+                self.logger.info("-" * 40)
+
+            result = {
+                "question": entry["question"],
+                "expected_answer": str(entry["answer"]),
+                "best_model_answer": model_answer,
+                "best_full_model_response": response,
+                "best_score": score,
+                "mean_score": score,
+                "completions": [{"model_answer": model_answer, "full_model_response": response, "score": score}],
+            }
+
+            return result
+
+        except Exception as e:
+            self.logger.error(f"Error processing entry {entry_index}:")
+            self.logger.error(f"Question: {entry['question']}")
+            self.logger.error(f"Error: {str(e)}")
+            return {
+                "question": entry["question"],
+                "expected_answer": str(entry["answer"]),
+                "best_model_answer": None,
+                "best_full_model_response": None,
+                "best_score": 0.0,
+                "mean_score": 0.0,
+                "error": str(e),
+                "completions": [],
+            }
+
+    def evaluate_dataset(self, category_name: str, dataset_config: DatasetConfig) -> dict[str, Any]:
+        """Evaluate a single dataset.
+
+        Args:
+            category_name: Name of the category
+            dataset_config: Configuration for the dataset
+
+        Returns:
+            Dict with evaluation results
+        """
+        dataset_name = dataset_config.dataset
+        self.logger.info(f"Evaluating dataset: {dataset_name}")
+
+        try:
+            # Create dataset
+            dataset_params = {**dataset_config.params, "size": dataset_config.size, "seed": dataset_config.seed}
+            dataset = reasoning_gym.create_dataset(dataset_name, **dataset_params)
+
+            # Get all entries
+            all_entries = list(dataset)
+
+            # Process entries with progress bar
+            results = []
+            for idx, entry in tqdm(enumerate(all_entries), desc=f"Processing {dataset_name}"):
+                result = self.process_entry(dataset, entry, idx, dataset_name)
+                results.append(result)
+
+            # Calculate metrics
+            total_score = sum(r["best_score"] for r in results)
+            average_score = total_score / len(results) if results else 0
+
+            return {
+                "name": dataset_name,
+                "category": category_name,
+                "average_best_score": average_score,
+                "average_mean_score": average_score,
+                "total_examples": len(results),
+                "config": dataset_params,
+                "system_prompt": self.config.get_system_prompt(),
+                "results": results,
+            }
+
+        except Exception as e:
+            self.logger.error(f"Error evaluating dataset {dataset_name}: {str(e)}")
+            return {
+                "name": dataset_name,
+                "category": category_name,
+                "average_best_score": 0.0,
+                "average_mean_score": 0.0,
+                "total_examples": 0,
+                "config": dataset_params,
+                "system_prompt": self.config.get_system_prompt(),
+                "error": str(e),
+                "results": [],
+            }
+
+    def evaluate_all(self) -> dict[str, Any]:
+        """Evaluate all categories and datasets.
+
+        Returns:
+            Dict with evaluation results
+        """
+        self.logger.info(f"Starting evaluation of {len(self.config.categories)} categories")
+
+        category_results = []
+        for category in self.config.categories:
+            self.logger.info(f"Evaluating category: {category.category}")
+
+            dataset_results = []
+            for dataset_config in category.datasets:
+                result = self.evaluate_dataset(category.category, dataset_config)
+                dataset_results.append(result)
+
+            category_results.append({"name": category.category, "datasets": dataset_results})
+
+        results = {
+            "metadata": {
+                "timestamp": self.start_time.isoformat(),
+                "model": self.config.model,
+                "device": self.device,
+                "duration_seconds": (datetime.now() - self.start_time).total_seconds(),
+                "max_tokens": self.config.max_tokens,
+                "temperature": self.config.temperature,
+                "top_p": self.config.top_p,
+            },
+            "categories": category_results,
+        }
+
+        return results
+
+
+def main():
+    """Entry point."""
+    parser = argparse.ArgumentParser(description="Evaluate local models on reasoning datasets")
+    parser.add_argument("--model-path", required=True, help="Path to base model or model identifier")
+    parser.add_argument("--hf-path", help="Path to HuggingFace checkpoint directory containing trained weights")
+    parser.add_argument("--config", required=True, help="Path to configuration file")
+    parser.add_argument("--output-dir", help="Override output directory specified in config")
+    parser.add_argument("--category", help="Evaluate only datasets from this category")
+    parser.add_argument("--device", default="cuda:0", help="Device to run model on")
+    parser.add_argument("--batch-size", type=int, default=1, help="Batch size for inference")
+    parser.add_argument("--verbose", action="store_true", help="Print detailed responses")
+    parser.add_argument("--debug", action="store_true", help="Enable debug logging")
+
+    args = parser.parse_args()
+
+    # Configure logging
+    logging.basicConfig(
+        level=logging.DEBUG if args.debug else logging.INFO,
+        format="%(asctime)s - %(name)s - %(levelname)s - %(message)s",
+    )
+
+    # Load configuration using our new config class
+    config = EvalConfig.from_yaml(args.config)
+
+    # Apply command line overrides
+    if args.output_dir:
+        config.output_dir = args.output_dir
+    if args.hf_path:
+        config.hf_path = args.hf_path
+
+    # Filter categories if specified
+    if args.category:
+        config.categories = [cat for cat in config.categories if cat.category == args.category]
+        if not config.categories:
+            print(f"Error: Category '{args.category}' not found in configuration")
+            return 1
+
+    try:
+        # Create evaluator
+        evaluator = LocalModelEvaluator(
+            model_path=args.model_path,
+            config=config,
+            hf_path=args.hf_path,
+            device=args.device,
+            batch_size=args.batch_size,
+            verbose=args.verbose,
+            debug=args.debug,
+        )
+
+        # Run evaluation
+        results = evaluator.evaluate_all()
+
+        # Create output directory
+        timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
+        output_dir = Path(config.output_dir) / f"local_{timestamp}"
+        output_dir.mkdir(parents=True, exist_ok=True)
+
+        # Save results
+        results_path = output_dir / "results.json"
+        with open(results_path, "w") as f:
+            json.dump(results, f, indent=2)
+
+        print(f"\nResults saved to: {results_path}")
+        return 0
+
+    except Exception as e:
+        print(f"Error during evaluation: {str(e)}")
+        if args.debug:
+            import traceback
+
+            traceback.print_exc()
+        return 1
+
+
+if __name__ == "__main__":
+    sys.exit(main())