Researchers have developed a new method called Reasoning State Propagation (RSP) to improve the training of process reward models (PRMs) for AI reasoning. RSP addresses the challenge of costly process annotations by effectively using outcome supervision to guide the learning of intermediate reasoning states. By modeling transitions between validity states in a reasoning trajectory, RSP connects intermediate steps to the final outcome, leading to improved performance in tasks like beam search and reinforcement learning. In evaluations, RSP showed average improvements of 5.6% in beam search and 2.1% in reinforcement learning when compared to the Qwen2.5-Math-PRM baseline. AI
IMPACT Enhances AI reasoning capabilities by improving training efficiency and effectiveness for intermediate steps.
RANK_REASON The cluster contains a research paper detailing a new method for AI training. [lever_c_demoted from research: ic=1 ai=1.0]
- alphaXiv
- arXiv
- CatalyzeX
- CORE Recommender
- DagsHub
- Gotit.pub
- Hugging Face
- Influence Flower
- Qwen2.5-Math-PRM
- Reasoning State Propagation
- ScienceCast
AI-generated summary · Google Gemini · from 1 sources. How we write summaries →
