We propose Re-FORC, an adaptive reward prediction method that, given a context, 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 26% while maintaining accuracy, 2) optimized model and thinking length selection that achieves 4% higher accuracy at equal compute and 55% less compute at equal accuracy compared to the largest model, 3) adaptive test-time scaling, which increases accuracy by 11% in high compute regime, and 7% in low compute regime. Re-FORC allows dynamic reasoning with length control via cost-per-token thresholds while estimating computation time upfront.
@article{arxiv.2511.02130,
title = {Re-FORC: Adaptive Reward Prediction for Efficient Chain-of-Thought Reasoning},
author = {Renos Zabounidis and Aditya Golatkar and Michael Kleinman and Alessandro Achille and Wei Xia and Stefano Soatto},
journal= {arXiv preprint arXiv:2511.02130},
year = {2025}
}
Comments
Accepted at Efficient Reasoning Workshop at NeurIPS 2025