CAST: Game Solvers as Turn-Level Teachers for LLM Agents
Training large language models (LLMs) to act in long-horizon games is a promising step toward generalist decision-making, yet reinforcement learning with verifiable rewards (RLVR) relies on sparse final rewards that reveal little about which decisions determine success. Denser process signals could supply this missing turn-level credit, but existing sources are hard to keep both cheap and accurate. We observe that changes in a game solver's state value reveal whether an action advances the state toward success. Building on this insight, we propose CAST (Credit Assignment from Solver Teachers), which converts these value changes into solver advantages and injects them into RLVR as turn-lev...
| Authors | Yu Wang, Yi-Kai Zhang, Wentao Shi et al. |
|---|---|
| Primary category | cs.CL |
| Published | 28 July 2026 |
| Last updated at source | 28 July 2026 |
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