Training Long-Context, Multi-Turn Software Engineering Agents with
  Reinforcement Learning
Authors: name: Alexander Golubev, name: Maria Trofimova, name: Sergei Polezhaev, name: Ibragim Badertdinov, name: Maksim Nekrashevich, name: Anton Shevtsov, name: Simon Karasik, name: Sergey Abramov, name: Andrei Andriushchenko, name: Filipp Fisin, name: Sergei Skvortsov, name: Boris Yangel
Organization: Nebius

Research on applications of Reinforcement Learning (RL) to Large Language
Models (LLMs) has mostly been focused on single-turn problems, such as
mathematical reasoning or single-shot code generation. While these problems can
be viewed as token-level multi-turn MDPs, this view corresponds to a degenerate
case of multi-turn interaction where the environment provides no feedback. This
contrasts with many real-world domains, such as software engineering (SWE),
which require rich multi-turn interactions with a stateful environment that
responds to each action with a non-trivial observation.
  To bridge this gap, we demonstrate the successful application of RL to this
general regime. Using a modified Decoupled Advantage Policy Optimization (DAPO)
algorithm, we train an agent based on Qwen2.5-72B-Instruct to solve real-world
software engineering tasks. Our approach increases the agent's success rate on
the SWE-bench Verified benchmark from a 20% rejection fine-tuned baseline to
39%, without relying on any teacher models. On SWE-rebench, our agent matches
or outperforms leading open-weight models such as DeepSeek-V3-0324 and
Qwen3-235B-A22B using an identical scaffolding, offering a viable path toward
building more capable autonomous agents for complex real-world problems based
on open models.