Dual-Track CoT: Budget-Aware Stepwise Guidance for Small LMs
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 wh...
| Authors | Sagnik Chatterjee, Atharva Patil, Sricharan Ramesh |
|---|---|
| Primary category | cs.CL |
| Published | 27 April 2026 |
| Last updated at source | 27 April 2026 |
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