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

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ethics.ai (27 July 2026), “CAST: Game Solvers as Turn-Level Teachers for LLM Agents,” evidence record 14532, https://ethics.ai/record/14532 (originally published by HuggingFace Daily Papers).

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