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INFUSER framework boosts LLM reasoning via guided self-evolution

By PulseAugur Editorial · [2 sources] ·

Researchers have developed INFUSER, a novel framework for self-evolving language models that enhances reasoning capabilities. This iterative co-training system features a Generator that creates questions and answers from documents, and a Solver that learns from them. The Generator is rewarded based on an influence score, ensuring it produces questions that genuinely improve the Solver's performance, rather than just difficult ones. INFUSER demonstrated significant improvements, with an 8B model outperforming a larger 32B model on math and coding tasks. AI

IMPACT Enhances LLM reasoning capabilities by creating adaptive training curricula, potentially leading to more capable AI agents.

RANK_REASON The cluster contains a research paper detailing a new method for improving language model reasoning.

Read on arXiv stat.ML →

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

INFUSER framework boosts LLM reasoning via guided self-evolution

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The cluster contains a research paper detailing a new method for improving language model reasoning.
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COVERAGE [2]

  1. arXiv stat.ML TIER_1 English(EN) · Siyu Chen, Miao Lu, Beining Wu, Heejune Sheen, Fengzhuo Zhang, Shuangning Li, Zhiyuan Li, Jose Blanchet, Tianhao Wang, Zhuoran Yang ·

    INFUSER: Influence-Guided Self-Evolution Improves Reasoning

    arXiv:2606.09052v1 Announce Type: cross Abstract: Self-evolution offers a scalable path to stronger reasoning: a pretrained language model improves itself with only minimal external supervision. Yet existing methods either depend on extensively curated or teacher-generated traini…

  2. arXiv stat.ML TIER_1 English(EN) · Zhuoran Yang ·

    INFUSER: Influence-Guided Self-Evolution Improves Reasoning

    Self-evolution offers a scalable path to stronger reasoning: a pretrained language model improves itself with only minimal external supervision. Yet existing methods either depend on extensively curated or teacher-generated training data, or, when the generator runs unsupervised,…

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