Entropy-Guided Loop: Achieving Reasoning through Uncertainty-Aware Generation
This provides a practical solution for production deployments where balancing quality and cost is critical, though it is incremental as it builds on existing uncertainty-aware methods.
The paper tackles the high cost and latency of reasoning models by introducing an entropy-guided refinement loop that uses token-level uncertainty to trigger targeted corrective edits, achieving 95% of a reference model's quality at one-third the cost and improving accuracy by 16 percentage points over single-pass inference.
Reasoning models often outperform smaller models but at 3--5$\times$ higher cost and added latency. We present entropy-guided refinement: a lightweight, test-time loop that uses token-level uncertainty to trigger a single, targeted refinement pass. We extract logprobs, compute Shannon entropy on top-$k$ alternatives, and apply a simple OR-logic trigger over perplexity, maximum token entropy, and low-confidence-token count. Unlike approaches that use entropy only for measurement or decoding, we pass a compact uncertainty report (tokens, confidences, alternatives, context) back to the model to guide corrective edits. On representative technical queries across reasoning, mathematics, and code generation tasks, a small model with our loop approaches 95\% of a reference reasoning model's quality at approximately one-third of the cost. The method achieves selective refinement on ~31\% of responses while improving accuracy by 16 percentage points over single-pass inference. We demonstrate that this uncertainty-aware loop provides an effective middle ground between single-pass inference and expensive reasoning chains, making it practical for production deployments where both quality and cost matter.