Dual-Track CoT: Budget-Aware Stepwise Guidance for Small LMs

• URL: http://arxiv.org/abs/2604.25039v1
• Date: Mon, 27 Apr 2026 22:43:33 GMT
• Title: Dual-Track CoT: Budget-Aware Stepwise Guidance for Small LMs
• Abstract summary: Small Language Models struggle with multi-step reasoning under tight compute and token budgets.<n>Existing test time reasoning methods such as self consistency improve performance, but often at high token cost and without fine-grained step-level control.<n>This project aims to address that gap: can Small Language Models (SLMs) reason reliably using the same or fewer tokens?
• Score: 0.3823356975862005
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Large Language Models (LLMs) solve many reasoning tasks via chain-of-thought (CoT) prompting, but smaller models (about 7 to 8B parameters) still struggle with multi-step reasoning under tight compute and token budgets. Existing test time reasoning methods such as self consistency (sampling multiple rationales and voting), Tree-of-Thoughts (search over intermediate thoughts), and critique revise loops improve performance, but often at high token cost and without fine-grained step-level control. This project1 aims to address that gap: can Small Language Models (SLMs) reason reliably using the same or fewer tokens? This question is both scientific and practical. Scientifically, it probes whether process supervision and simple test-time controls (such as token budgets and rejection of redundant steps) can substitute for model scale or large sampling counts. Practically, many deployments (on-device, low-latency, or cost-constrained settings) cannot afford huge models or dozens of sampled rationales per query. A method that improves SLM reasoning at fixed cost would therefore be directly useful.

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