RASPRef: Retrieval-Augmented Self-Supervised Prompt Refinement for Large Reasoning Models
Recent reasoning-focused language models such as DeepSeek R1 and OpenAI o1 have demonstrated strong performance on structured reasoning benchmarks including GSM8K, MATH, and multi-hop question answering tasks. However, their performance remains highly sensitive to prompt formulation, and designing effective prompts is typically a manual and iterative process that does not scale well across tasks or domains. To address this limitation, we introduce Retrieval-Augmented Self-Supervised Prompt Refinement (RASPRef), a framework that improves prompts without requiring human annotations or task-specific supervision. The approach retrieves relevant examples and previously generated reasoning traj...
| Authors | Rahul Soni |
|---|---|
| Primary category | cs.CL |
| Published | 27 March 2026 |
| Last updated at source | 27 March 2026 |
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