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CompassPlay introduces a novel self-play training method that rewards task proposers based on gradient alignment rather than just success rates, significantly improving training efficiency in coding and theorem proving benchmarks. By leveraging first-order approximations to learning progress, this approach reduces GPU-hour requirements while enhancing model performance across in-domain and out-of-domain tasks.
CompassPlay: Rewarding the Proposer for Where It Moves the Solver
arXiv:2609.32228v1 Announce Type: new Abstract: In self-play, a proposer generates verifiable tasks to train a solver. Proposer rewards often depend on the solver's success rate, but equally difficult tasks can differ in their training value. We introduce CompassPlay, a self-play method that rewards the proposer through gradient alignment. The reward favors tasks whose solver loss gradients align with those of reference tasks representing the target capabilities. It draws on a first-order approximation to learning progress and scores each eligible task without additional solver training. Our experiments show gains in performance and training efficiency. In coding self-play with Qwen2.5-Coder-7B, a small external reference set guides task generation. CompassPlay improves average accuracy over AZR's difficulty reward by 1.5 percentage points on in-domain coding and 2.7 on out-of-domain mathematics. In Lean4 theorem proving, CompassPlay matches the difficulty baseline's 150-iteration cumulative coverage with 40\% fewer GPU-hours.
Abstract:In self-play, a proposer generates verifiable tasks to train a solver. Proposer rewards often depend on the solver's success rate, but equally difficult tasks can differ in their training value. We introduce CompassPlay, a self-play method that rewards the proposer through gradient alignment. The reward favors tasks whose solver loss gradients align with those of reference tasks representing the target capabilities. It draws on a first-order approximation to learning progress and scores each eligible task without additional solver training. Our experiments show gains in performance and training efficiency. In coding self-play with Qwen2.5-Coder-7B, a small external reference set guides task generation. CompassPlay improves average accuracy over AZR's difficulty reward by 1.5 percentage points on in-domain coding and 2.7 on out-of-domain mathematics. In Lean4 theorem proving, CompassPlay matches the difficulty baseline's 150-iteration cumulative coverage with 40\% fewer GPU-hours.
Machine Learning (cs.LG)
arXiv:2609.32228 [cs.LG]
(or arXiv:2609.32228v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2609.32228 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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