Self-Hinting Language Models Enhance Reinforcement Learning research paper by Microsoft, 2026

Microsoft · Inference and efficiency · Feb 3, 2026 · 21 citations · 31 upvotes · unverified

SAGE is an on-policy reinforcement learning framework that enhances GRPO by injecting self-hints during training to increase outcome diversity under sparse rewards, improving alignment of large language models.

By Baohao Liao, Hanze Dong, Xinxing Xu and 2 more · arXiv 2602.03143 · PDF · Code

UnverifiedHugging Face's summary; not yet checked by hand.

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About this paper

Venue: arXiv.org
Citations: 21, 3 influential · Semantic Scholar
Upvotes: 31 · Hugging Face
Lab: Microsoft · on Companies · on Acquisitions · on Quarterly · on Paydays · on Releases · on TechConf

Changes

CitationsNov 2, 2026Citations rose from 17 to 21.

CitationsOct 12, 2026Citations rose from 16 to 17.

CitationsOct 5, 2026Citations rose from 15 to 16.

CitationsSep 25, 2026A first count of 3 influential citations.

CitationsSep 25, 2026A first count of 15 citations.

New paperSep 25, 2026Found by the weekly Hugging Face scan; not yet checked by hand.

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