OThink-SRR1: A New Framework for LLM Retrieval and Reasoning
A new framework named OThink-SRR1 has been introduced by researchers to enhance large language models through an iterative Search-Refine-Reason methodology, utilizing reinforcement learning for training. This framework tackles two significant issues in dynamic retrieval techniques: the distraction caused by irrelevant retrieved noise and the high computational costs associated with processing entire documents. During the crucial Refine phase, the framework condenses retrieved documents into succinct, pertinent facts prior to reasoning. The team also presents GRPO-IR, a comprehensive reinforcement learning algorithm that incentivizes accurate evidence selection while discouraging unnecessary retrievals. Testing on four multi-hop QA benchmarks demonstrated advancements compared to current methods. The research can be found on arXiv with the identifier 2604.19766.
Key facts
- OThink-SRR1 is a framework for large language models.
- It uses an iterative Search-Refine-Reason process.
- The Refine stage distills retrieved documents into concise facts.
- GRPO-IR is an end-to-end reinforcement learning algorithm.
- GRPO-IR rewards accurate evidence identification and penalizes excessive retrievals.
- Experiments were conducted on four multi-hop QA benchmarks.
- The paper is on arXiv with ID 2604.19766.
- The framework addresses noise and computational cost issues.
Entities
Institutions
- arXiv