Re-FORC: Adaptive Reward Prediction for Efficient Chain-of-Thought Reasoning

Abstract:We propose Re-FORC, an adaptive reward prediction method that, given a query, enables prediction of the expected future rewards as a function of the number of future thinking tokens. Re-FORC trains a lightweight adapter on reasoning models, demonstrating improved prediction with longer reasoning and larger models. Re-FORC enables: 1) early stopping of unpromising reasoning chains, reducing compute by up to 26\% compared to fixed-budget cutoffs, while maintaining accuracy, 2) optimized model and thinking length selection that outperforms the largest model alone--- reaching 1.7 percentage points higher peak accuracy while needing up to 12\% less compute to match the largest model's accuracy, 3) adaptive test-time scaling, which increases accuracy by 9.9 percentage points (on average at maximum compute) over confidence-based baselines. Re-FORC allows dynamic reasoning with length control via cost-per-token thresholds while estimating computation time upfront.

Artificial Intelligence (cs.AI); Machine Learning (cs.LG)

arXiv:2511.02130 [cs.AI]

(or arXiv:2511.02130v2 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2511.02130 Focus to learn more arXiv-issued DOI via DataCite

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