Learning from Self-Debate: Preparing Reasoning Models for Multi-Agent Debate

In three linesSDRL (Self-Debate Reinforcement Learning) trains LLMs to solve problems standalone AND benefit from multi-agent debate. The model samples multiple solutions, constructs debate context with diverse reasoning paths, then jointly optimizes initial and debate-conditioned responses. Results: consistent MAD performance gains across benchmarks and agent configurations.

Summary generated by Claude — human-verified