CAST: Game Solvers as Turn-Level Teachers for LLM Agents
CAST leverages game solver state value changes as turn-level credit signals to address sparse rewards in RLVR for LLM agents. It converts these value changes into "solver advantages" that are injected directly into the reinforcement learning process. Under a soft-optimal assumption, maximizing this advantage is mathematically equivalent to on-policy distillation from the solver using only scalar values. The method achieves state-of-the-art performance across Sokoban, Minesweeper, and Rush Hour,
Analysis
TL;DR
- CAST leverages game solver state value changes as turn-level credit signals to address sparse rewards in RLVR for LLM agents.
- It converts these value changes into "solver advantages" that are injected directly into the reinforcement learning process.
- Under a soft-optimal assumption, maximizing this advantage is mathematically equivalent to on-policy distillation from the solver using only scalar values.
- The method achieves state-of-the-art performance across Sokoban, Minesweeper, and Rush Hour, including zero-shot transfer to ALFWorld and WebShop.
Why It Matters
This work addresses a critical bottleneck in training LLMs for complex, long-horizon decision-making tasks: the scarcity of dense, accurate feedback during training. By utilizing existing game solvers as cheap and reliable teachers, CAST provides a scalable pathway to improve agent generalization without requiring expensive human annotation or complex reward engineering, making it highly relevant for developing robust AI agents in real-world environments.
Technical Details
- Core Mechanism: The method observes that when an LLM agent takes an action in a game environment, the change in the optimal game solver's estimated state value (from $V(s)$ to $V(s')$) indicates whether the move was beneficial toward solving the puzzle.
- Solver Advantage Calculation: This delta is transformed into a "solver advantage" signal ($A_{solver} = V(s') - V(s)$), which serves as a dense reward for the specific turn, replacing or supplementing the sparse final success/failure reward.
- Theoretical Equivalence: The authors prove that under the assumption that the solver behaves near-optimally (soft-optimal), training the policy to maximize this solver advantage is theoretically equivalent to distilling knowledge from the solver's policy, but significantly more efficient as it requires only scalar value estimates rather than full probability distributions (logits).
- Evaluation Scope: The approach was validated on three distinct puzzle domains—Sokoban, Minesweeper, and Rush Hour—demonstrating superior performance over trained baselines on both seen difficulties and unseen test cases, alongside strong zero-shot results on the ALFWorld and WebShop benchmarks.
Industry Insight
- Cost-Efficient Training Pipelines: Organizations can leverage pre-existing, rule-based solvers or search algorithms common in specific domains (e.g., logistics, circuit design) to train powerful LLM agents without building custom reward functions or collecting massive labeled datasets, drastically reducing development costs.
- Generalization via Value Signals: Relying on value function differences rather than raw actions for supervision encourages agents to learn underlying strategic principles, potentially leading to better zero-shot transfer capabilities to new levels or slightly modified environments compared to standard imitation learning.
- Hybrid Architecture Potential: This framework suggests a practical architecture where a fast, imperfect heuristic solver guides the training of a slower, more flexible LLM agent, combining the reliability of classical planning with the adaptability of neural networks for complex decision-making tasks.
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