Reinforcement learning for code generation typically depends on unit-test pass rates as verifiable rewards. In practice, this creates three persistent issues: Static golden tests are limited in ...

Reinforcement learning has become the central approach for language models (LMs) to learn from environmental reward or feedback. In practice, the environmental feedback is usually sparse and delayed.

Abstract: Code optimization is a crucial task that aims to enhance code performance. However, this process is often tedious and complex, highlighting the necessity for automatic code optimization ...

Abstract: Repository-level code completion aims to generate code for unfinished code snippets within the context of a specified repository. Existing approaches mainly rely on retrievalaugmented ...

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AI agents are reshaping software development, from writing code to carrying out complex instructions. Yet LLM-based agents are prone to errors and often perform poorly on complicated, multi-step tasks ...

AgiBot announced a key milestone this week with the successful deployment of its Real-World Reinforcement Learning system in a manufacturing pilot with Longcheer Technology. The pilot project marks ...

Reinforcement learning (RL) is machine learning (ML) in which the learning system adjusts its behavior to maximize the amount of reward and minimize the amount of punishment it receives over time ...

In a giant feat of genetic engineering, scientists have created bacteria that make proteins in a radically different way than all natural species do. By Carl Zimmer At the heart of all life is a code.

Reinforcement learning (RL) is a branch of machine learning in which an agent learns to make sequences of decisions by interacting with an environment and maximising cumulative rewards. Unlike ...