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New RLIF framework uses multi-reward signals to improve LLM reasoning

By PulseAugur Editorial · [2 sources] ·

Researchers have developed a new framework for training large language models using Reinforcement Learning from Internal Feedback (RLIF). This multi-reward approach decomposes the training signal into an answer-level reward from cluster voting and a completion-level reward based on token self-certainty. The method incorporates GDPO-based normalization and KL-Cov regularization to enhance stability and prevent collapse, achieving performance close to supervised methods without external ground-truth supervision. AI

IMPACT This new RLIF framework offers a more stable and robust unsupervised training method for LLMs, potentially improving their reasoning capabilities without relying on external human supervision.

RANK_REASON The cluster contains an academic paper detailing a new method for training LLMs.

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

COVERAGE [2]

  1. arXiv cs.CL TIER_1 English(EN) · Shourov Joarder, Diganta Sikdar, Ahsan Habib Akash, Binod Bhattarai, Prashnna Gyawali ·

    Two is better than one: A Collapse-free Multi-Reward RLIF Training Framework

    arXiv:2605.22620v1 Announce Type: cross Abstract: Reinforcement learning with verifiable rewards (RLVR) has substantially improved the reasoning ability of LLMs, but often depends on external supervision from human annotations or gold-standard solutions. Reinforcement learning fr…

  2. arXiv cs.CL TIER_1 English(EN) · Prashnna Gyawali ·

    Two is better than one: A Collapse-free Multi-Reward RLIF Training Framework

    Reinforcement learning with verifiable rewards (RLVR) has substantially improved the reasoning ability of LLMs, but often depends on external supervision from human annotations or gold-standard solutions. Reinforcement learning from internal feedback (RLIF) has recently emerged a…

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