Rl sandbox intro

[arbrown]

No description provided.

[gemini-code-assist]

Code Review

This pull request introduces a High-Performance Distributed RL Sandbox environment on GKE, adding Dockerfiles, Kubernetes configurations, and a training script using TRL and Ray. Key feedback includes adding a timeout to the HTTP request, using --no-install-recommends to reduce Docker image size, enabling vLLM in the GRPO configuration for faster generation, making the bash block regex more robust, and moving ray.init() to the main function to prevent initialization issues on remote workers.

[gemini-code-assist]

The urllib.request.urlopen call does not specify a timeout. If the GitHub raw server is slow or unresponsive, this call can block indefinitely, hanging the dataset mapping process. It is recommended to set a reasonable timeout.

Suggested change

	with urllib.request.urlopen(url) as response:
	with urllib.request.urlopen(url, timeout=10) as response:

[gemini-code-assist]

To keep the Docker image size as small as possible, it is recommended to use the --no-install-recommends flag with apt-get install.

RUN apt-get update && apt-get install -y --no-install-recommends \
    git \
    build-essential \
    libsqlite3-dev \
    && rm -rf /var/lib/apt/lists/*

[gemini-code-assist]

Since the GPU worker base image is vllm/vllm-openai, you can significantly accelerate the generation phase of GRPO by enabling vLLM integration in GRPOConfig using use_vllm=True.

Suggested change

	training_args = GRPOConfig(
	output_dir="outputs",
	learning_rate=5e-6,
	max_steps=10,
	per_device_train_batch_size=1,
	gradient_accumulation_steps=4,
	num_generations=8,
	generation_batch_size=8,
	)
	training_args = GRPOConfig(
	output_dir="outputs",
	learning_rate=5e-6,
	max_steps=10,
	per_device_train_batch_size=1,
	gradient_accumulation_steps=4,
	num_generations=8,
	generation_batch_size=8,
	use_vllm=True,
	)

[gemini-code-assist]

LLMs frequently output sh instead of bash, or include trailing whitespace after the language identifier. The current regex is strict and will fail to match these variations, resulting in a 0.0 reward for otherwise valid completions. Using a more permissive regex like r"```(?:bash|sh)\\s*\\n(.*?)\\n```" is much more robust.

Suggested change

	bash_match = re.search(r"```bash\n(.*?)\n```", code, re.DOTALL)
	bash_match = re.search(r"```(?:bash|sh)\\s*\\n(.*?)\\n```", code, re.DOTALL)

[gemini-code-assist]

Calling ray.init() at the module level is a Ray anti-pattern. When Ray workers import this module to execute tasks, they will run ray.init() again. Although ignore_reinit_error=True suppresses the error, it can still cause unexpected behavior or warnings. It is best practice to initialize Ray inside the main() function or under the if __name__ == "__main__": block.

[gemini-code-assist]

Initialize Ray inside the main() function to ensure it only runs on the driver process and not on the Ray workers when they import this module.

Suggested change

	def main():
	print("Submitting training job to GPU worker...")
	ray.get(train.remote())
	def main():
	ray.init(ignore_reinit_error=True)
	print("Submitting training job to GPU worker...")
	ray.get(train.remote())