Survive or Collapse: The Asymmetric Roles of Data Gating and Reward Grounding in Self-Play RL
arXiv:2605.22217v1 Announce Type: new Abstract: Self-play reinforcement learning trains language models on their own generated tasks, co-evolving a proposer and solver without human labels. Recent systems report strong reasoning gains, but collapse and instability are widely observed and poorly understood. The dominant response treats this as a reward-design problem. We argue instead that self-play stability is governed by two distinct levers: a data-level gate that decides which proposer-generated tasks enter the training pool, and the reward signal that updates the policy on tasks already ad
This research addresses a critical limitation in self-play reinforcement learning, a technique central to advanced AI development, at a time when 'collapse and instability' are widely observed yet poorly understood.
Understanding and mitigating instability in self-play RL directly impacts the scalability and reliability of advanced language models, influencing the trajectory of AI agent development.
The focus for self-play RL stability shifts from primarily reward design to explicitly integrating 'data-level gating' and 'reward grounding' as causal levers, offering new avenues for robust AI training.
- · AI research labs
- · developers of large language models
- · AI agent developers
- · AI teams using brittle self-play methods
- · organizations dependent on unstable AI agent systems
More stable and performant self-play training regimes for large language models will emerge.
The improved robustness of self-play systems could accelerate the deployment and capabilities of autonomous AI agents.
Enhanced AI agent reliability might lead to faster automation of complex tasks, impacting white-collar workflows sooner than anticipated.
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Read at arXiv cs.LG