RLEF: Grounding Code LLMs in Execution Feedback with Reinforcement Learning
FreeGrounding Code LLMs in Execution Feedback
FreeFree tier
Outputs: code
About RLEF: Grounding Code LLMs in Execution Feedback with Reinforcement Learning
RLEF (Reinforcement Learning from Execution Feedback) is a research method proposed by Jonas Gehring and co-authors that uses end-to-end reinforcement learning to ground large language models in execution feedback for code synthesis. It specifically addresses the challenge that state-of-the-art LLMs often fail to iteratively improve code from feedback. The method is benchmarked on competitive programming tasks, where it achieves new state-of-the-art results with both 8B and 70B parameter models while reducing the number of required samples by an order of magnitude. Analysis shows that RLEF-trained models effectively leverage automatic feedback over multiple steps, improving inference-time behavior.
Key Features
End-to-end reinforcement learning for code synthesis
Teaches models to leverage execution feedback
Improves iterative code refinement compared to independent sampling
Achieves state-of-the-art on competitive programming with 8B and 70B models
Reduces required samples by an order of magnitude
Evaluated on automatic feedback over multiple steps
Pros & Cons
Pros
- Achieves state-of-the-art results with both small (8B) and large (70B) models
- Reduces the number of samples needed by an order of magnitude
- Effectively uses automatic execution feedback over multiple steps
- Novel end-to-end RL approach for iterative code generation
Cons
- Only evaluated on competitive programming tasks; generalization to other code domains is not shown
- Requires an execution environment to provide feedback
- As a research method, may need adaptation for production use
- Dependence on high-quality execution feedback for training
Best For
Competitive programmingCode synthesisIterative code improvementResearch on reinforcement learning for code LLMs
FAQ
What does RLEF stand for?
RLEF stands for Reinforcement Learning from Execution Feedback, a method for grounding code LLMs in execution feedback using reinforcement learning.
How does RLEF improve code generation?
RLEF uses end-to-end reinforcement learning to teach models to leverage execution feedback, enabling them to iteratively improve code based on automatic feedback, which reduces the number of samples needed and achieves state-of-the-art results on competitive programming tasks.