RAG over Thinking Traces Can Improve Reasoning Tasks
Retrieval-augmented generation (RAG) has proven effective for knowledge-intensive tasks, but is widely believed to offer limited benefit for reasoning-intensive problems such as math and code generation. We challenge this assumption by showing that the limitation lies not in RAG itself, but in the choice of corpus. Instead of retrieving documents, we propose retrieving thinking traces, i.e., intermediate thinking trajectories generated during problem solving attempts. We show that thinking traces are already a strong retrieval source, and further introduce T3, an offline method that transforms them into structured, retrieval-friendly representations, to improve usability. Using these trac...
| Authors | Negar Arabzadeh, Wenjie Ma, Sewon Min et al. |
|---|---|
| Primary category | cs.IR |
| Published | 5 May 2026 |
| Last updated at source | 6 September 2026 |
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