Re-FORC: Adaptive Reward Prediction for Efficient Chain-of-Thought Reasoning
This addresses the challenge of computational inefficiency in reasoning models for AI applications, representing an incremental improvement with specific optimizations.
The paper tackles the problem of inefficient chain-of-thought reasoning by proposing Re-FORC, an adaptive reward prediction method that reduces compute by 26% while maintaining accuracy and improves accuracy by up to 11% in different compute regimes.
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.