ThinkRetrieve: Retrieval-Augmented Reasoning Traces for Test-Time Scaling

TL;DR - ThinkRetrieve is a test-time scaling framework that injects dynamically retrieved solved exemplars into a reasoning model's chain-of-thought at each intermediate step, countering the diminishing or negative returns of simply generating longer traces. It matters because it reframes retrieval as guidance on how to reason rather than what facts to recall.

• Motivation: sequential test-time scaling degrades as traces lengthen, due to rising uncertainty, error compounding, and drift from the original problem.
• Method: given an external corpus of problems paired with step-by-step solutions, relevant exemplars are retrieved at each reasoning step and injected directly into the thinking trace.
• Evaluation: five reasoning models spanning 1.5B–8B parameters on GSM-8K, MATH-500, AIME 2025, and SciQ.
• Results: consistent accuracy gains over standard test-time scaling, with relative improvements up to 60% on AIME 2025.