New RLVR Framework PACS Enhances LLM Reasoning Capabilities

Researchers have introduced PACS, a novel framework for Reinforcement Learning with Verifiable Rewards (RLVR) designed to improve the reasoning capabilities of large language models (LLMs). PACS reformulates RLVR as a supervised learning task, optimizing a score function using cross-entropy loss, which inherently recovers stable policy gradient updates. Experiments show PACS significantly outperforms existing open-source models and RLVR baselines, with notable gains of over 8% and 9% on 4B and 8B models, respectively. AI

IMPACT This framework could lead to more robust and efficient LLMs for complex reasoning tasks.

RANK_REASON The cluster contains an academic paper detailing a new framework for improving LLM capabilities. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

• arXiv
• Hugging Face
• Jiaming Li
• large language models
• Reinforcement Learning with Verifiable Rewards

• paper
• model release

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