Add E2B code interpreter reward function

README.md

@@ -170,6 +170,35 @@ ACCELERATE_LOG_LEVEL=info accelerate launch --config_file recipes/accelerate_con
 
 Our final [model](https://huggingface.co/Dongwei/Qwen-2.5-7B_Base_Math_smalllr), while using different learning rates, loss functions and reward structures, achieves 69.4% accuracy on MATH-500, demonstrating a 17%+ improvement over the base model.
 
+#### Training with a code interpreter
+
+We provide a `code` reward function for executing code generated by the policy during training. Currently, this reward function targets code contests like [Codeforces](https://codeforces.com), where solutions are executed against a set of test cases and the overall success rate is returned as the final reward. To ensure safe execution, we use [E2B](https://e2b.dev) sandboxes, which are fast and cheap to run. To use this reward function, first install the necessary dependencies:
+
+```shell
+uv pip install -e '.[code]
+```
+
+Then create a `.env` file and place an API token from E2B within it:
+
+```
+E2B_API_KEY="e2b_xxx"
+```
+
+Then make sure your dataset contains a `verification_info` column with the following schema (adopted from PrimeIntellect's excellent [datasets](https://huggingface.co/collections/PrimeIntellect/synthetic-1-67a2c399cfdd6c9f7fae0c37) of verifiable problems):
+
+```python
+{
+    "language": "python",
+    "test_cases": [
+        {
+            "input": "4\n4\n0001\n1000\n0011\n0111\n3\n010\n101\n0\n2\n00000\n00001\n4\n01\n001\n0001\n00001\n",
+            "output": "1\n3 \n-1\n0\n\n2\n1 2 \n",
+            "type": "stdin_stdout",
+        }
+    ],
+}
+```
+
 ### Launching jobs on a Slurm cluster
 
 If you have access to a Slurm cluster, we provide a `slurm/train.slurm` script that will automatically queue training jobs for you. Here's how you can use it:

recipes/Qwen2.5-1.5B-Instruct/grpo/config_demo_code.yaml

@@ -0,0 +1,59 @@
+# Model arguments
+model_name_or_path: Qwen/Qwen2.5-1.5B-Instruct
+model_revision: main
+torch_dtype: bfloat16
+attn_implementation: flash_attention_2
+
+# Data training arguments
+dataset_name: open-r1/verifiable-coding-problems-python-10k
+dataset_configs:
+- default
+system_prompt: "You are a helpful AI Assistant that provides well-reasoned and detailed responses. You first think about the reasoning process as an internal monologue and then provide the user with the answer. Respond in the following format: <think>\n...\n</think>\n<answer>\n...\n</answer>"
+
+# GRPO trainer config
+callbacks:
+- push_to_hub_revision
+beta: 0.01
+bf16: true
+use_vllm: true
+vllm_device: auto
+vllm_gpu_memory_utilization: 0.9
+do_eval: false
+gradient_accumulation_steps: 4
+gradient_checkpointing: true
+gradient_checkpointing_kwargs:
+  use_reentrant: false
+hub_model_id: Qwen2.5-1.5B-Open-R1-Code-GRPO
+hub_strategy: every_save
+learning_rate: 5.0e-07
+log_completions: true
+log_level: info
+logging_first_step: true
+logging_steps: 1
+logging_strategy: steps
+lr_scheduler_type: cosine_with_min_lr
+lr_scheduler_kwargs:
+  min_lr_rate: 0.1
+max_prompt_length: 1024
+max_completion_length: 2048
+max_steps: 500
+num_generations: 14
+num_train_epochs: 1
+output_dir: data/Qwen2.5-1.5B-Open-R1-Code-GRPO
+overwrite_output_dir: true
+per_device_train_batch_size: 16
+push_to_hub: true
+report_to:
+- wandb
+reward_funcs:
+- code
+- format
+reward_weights:
+- 1.0
+- 0.1
+save_strategy: "steps"
+save_steps: 50
+save_total_limit: 1
+seed: 42
+temperature: 1.0
+warmup_ratio: 0.03

setup.py

@@ -46,6 +46,7 @@
     "datasets>=3.2.0",
     "deepspeed==0.15.4",
     "distilabel[vllm,ray,openai]>=1.5.2",
+    "e2b-code-interpreter>=1.0.5",
     "einops>=0.8.0",
     "flake8>=6.0.0",
     "flash_attn>=2.7.4.post1",
@@ -60,6 +61,7 @@
     "parameterized>=0.9.0",
     "peft>=0.14.0",
     "pytest",
+    "python-dotenv",
     "ruff>=0.9.0",
     "safetensors>=0.3.3",
     "sentencepiece>=0.1.99",
@@ -88,6 +90,7 @@ def deps_list(*pkgs):
 extras["torch"] = deps_list("torch")
 extras["quality"] = deps_list("ruff", "isort", "flake8")
 extras["train"] = deps_list("flash_attn")
+extras["code"] = deps_list("e2b-code-interpreter", "python-dotenv")
 extras["eval"] = deps_list("lighteval", "math-verify")
 extras["dev"] = extras["quality"] + extras["tests"] + extras["eval"] + extras["train"]
 

src/open_r1/grpo.py

@@ -27,6 +27,7 @@
 from open_r1.configs import GRPOConfig
 from open_r1.rewards import (
     accuracy_reward,
+    code_reward,
     format_reward,
     get_cosine_scaled_reward,
     get_repetition_penalty_reward,
@@ -161,6 +162,7 @@ def main(script_args, training_args, model_args):
             max_penalty=script_args.repetition_max_penalty,
         ),
         "length": len_reward,
+        "code": code_reward,
     }
     reward_funcs = [REWARD_FUNCS_REGISTRY[func] for func in script_args.reward_funcs]
 

src/open_r1/rewards.py

@@ -1,12 +1,22 @@
 """Reward functions for GRPO training."""
 
+import json
 import math
 import re
 from typing import Dict
 
 from latex2sympy2_extended import NormalizationConfig
 from math_verify import LatexExtractionConfig, parse, verify
 
+from .utils import is_e2b_available
+
+
+if is_e2b_available():
+    from dotenv import load_dotenv
+    from e2b_code_interpreter import Sandbox
+
+    load_dotenv()
+
 
 def accuracy_reward(completions, solution, **kwargs):
     """Reward function that checks if the completion is the same as the ground truth."""
@@ -271,3 +281,80 @@ def repetition_penalty_reward(completions, **kwargs) -> float:
         return rewards
 
     return repetition_penalty_reward
+
+
+def extract_code(completion: str) -> str:
+    pattern = re.compile(r"```python\n(.*?)```", re.DOTALL)
+    matches = pattern.findall(completion)
+    extracted_answer = matches[-1] if len(matches) >= 1 else ""
+    return extracted_answer
+
+
+def code_reward(completions, **kwargs) -> list[float]:
+    """Reward function that evaluates code snippets using the E2B code interpreter.
+
+    Assumes the dataset contains a `verification_info` column with test cases.
+    """
+    if not is_e2b_available():
+        raise ImportError(
+            "E2B is not available and required for this reward function. Please install E2B with "
+            "`pip install e2b-code-interpreter` and add an API key to a `.env` file."
+        )
+
+    rewards = []
+    # TODO: add support for other languages in E2B: https://e2b.dev/docs/code-interpreting/supported-languages
+    try:
+        """Returns a reward function that evaluates code snippets in a sandbox."""
+        evaluation_script_template = """
+        import subprocess
+        import json
+
+        def evaluate_code(code, test_cases):
+            passed = 0
+            total = len(test_cases)
+            exec_timeout = 5
+
+            for case in test_cases:
+                process = subprocess.run(
+                    ["python3", "-c", code],
+                    input=case["input"],
+                    text=True,
+                    capture_output=True,
+                    timeout=exec_timeout
+                )
+
+                if process.returncode != 0:  # Error in execution
+                    continue
+
+                output = process.stdout.strip()
+                if output.strip() == case["output"].strip():
+                    passed += 1
+
+            success_rate = (passed / total)
+            return success_rate
+
+        code_snippet = {code}
+        test_cases = json.loads({test_cases})
+
+        evaluate_code(code_snippet, test_cases)
+        """
+        code_snippets = [extract_code(completion[-1]["content"]) for completion in completions]
+        verification_info = kwargs["verification_info"]
+        scripts = [
+            evaluation_script_template.format(
+                code=json.dumps(code), test_cases=json.dumps(json.dumps(info["test_cases"]))
+            )
+            for code, info in zip(code_snippets, verification_info)
+        ]
+        with Sandbox(timeout=30, request_timeout=3) as sbx:
+            for script in scripts:
+                execution = sbx.run_code(script, language=verification_info["language"])
+                try:
+                    output = float(execution.text)
+                except (TypeError, ValueError):
+                    output = 0.0
+                rewards.append(output)
+    except Exception as e:
+        print(f"Error from E2B executor: {e}")
+        rewards = [0.0] * len(completions)
+    return rewards

src/open_r1/utils/__init__.py

@@ -1,4 +1,5 @@
+from .import_utils import is_e2b_available
 from .model_utils import get_tokenizer
 
 
-__all__ = ["get_tokenizer"]
+__all__ = ["get_tokenizer", "is_e2b_available"]

src/open_r1/utils/import_utils.py

@@ -0,0 +1,23 @@
+# Copyright 2025 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from transformers.utils.import_utils import _is_package_available
+
+
+# Use same as transformers.utils.import_utils
+_e2b_available = _is_package_available("e2b")
+
+
+def is_e2b_available() -> bool:
+    return _e2b_available