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Reinforcement Learning Powers Advanced Software Engineering Agents

A new paper details a significant advancement in using reinforcement learning (RL) to train large language models (LLMs) for complex, multi-turn software engineering tasks. The research demonstrates that RL can equip open-weight LLMs to tackle real-world coding challenges, achieving performance that rivals or surpasses leading proprietary models.

Traditionally, LLMs have excelled at single-turn problems, like answering a single question or generating a short piece of code. However, software engineering (SWE) tasks are inherently multi-turn, requiring an agent to repeatedly interact with a dynamic environment, interpret feedback, and make a sequence of decisions. Think of a developer trying to fix a bug: they might write some code, run tests, see an error message, then modify the code again based on that feedback. This is a far cry from a simple, one-off request.

The researchers adapted a reinforcement learning algorithm called Decoupled Advantage Policy Optimization (DAPO) to handle these complex, stateful interactions. They trained a large model, Qwen2.5-72B-Instruct, on a dataset of over 7,000 curated software engineering tasks. These tasks involve fixing bugs based on natural language descriptions and failing test cases, all within simulated coding environments.

A Two-Phase Training Approach

The training process involved two main phases. First, a “rejection fine-tuning” (RFT) step was used to improve the model’s ability to follow instructions and format its actions correctly. This involved running the model on tasks and keeping only the successful interactions for further training. This initial step boosted the model’s success rate to about 20%.

The core of the work then involved applying RL over thousands of these tasks. This iterative process allowed the agent to learn from its interactions, gradually improving its policy for solving SWE problems. The researchers experimented with different context lengths, eventually scaling up to a massive 131,000 tokens, which is crucial for handling the long histories of interactions often required in software development.

Impressive Results

The results are striking. The RL-trained agent achieved a 39% success rate on the SWE-BENCH Verified benchmark, effectively doubling the performance of the baseline rejection-fine-tuned model. Critically, this performance matches or exceeds that of other top open-weight models, such as DeepSeek-V3-0324 and Qwen3-235B-A22B, on the SWE-REBENCH benchmark. This indicates that RL can indeed unlock more advanced capabilities in smaller, more accessible models.

Bridging the Gap to Real-World Applications

The paper highlights several key challenges in applying RL to such domains, including the long-horizon nature of the tasks, the need to interpret complex feedback (like compiler errors and test results), and the sparse nature of rewards (only getting a “success” signal at the very end). By addressing these challenges, the research offers a promising path towards building more capable autonomous agents that can tackle real-world software engineering problems, potentially automating tasks like debugging, code generation, and maintenance. The work suggests that RL is not just for simple games or math problems, but a powerful tool for complex, interactive AI agents.