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STEER

3 articles · Page 1 of 1
PaperAgent
PaperAgent
Sep 17, 2026 · Artificial Intelligence

LLM Post-Training Paradigm Shift: 5 New Paths Replacing Monolithic RL

This article analyzes five fundamental shifts in LLM post-training over the past six months: moving from monolithic RL to expert distillation (MOPD), online distillation as a 10x cheaper RL alternative, refined RLVR techniques addressing entropy collapse and exploration, SFT-RL distribution alignment, and data quality as an irrecoverable hard constraint.

LLM post-trainingMiMo-V2-FlashRLVR
0 likes · 9 min read
LLM Post-Training Paradigm Shift: 5 New Paths Replacing Monolithic RL
Machine Learning Algorithms & Natural Language Processing
Machine Learning Algorithms & Natural Language Processing
Jul 24, 2026 · Artificial Intelligence

Why Large-Model RL Training Narrows Over Time? ACL 2026 Paper Reveals Entropy Collapse

The article analyzes why reinforcement learning with verifiable rewards (RLVR) for large models experiences rapid policy‑entropy collapse, breaks the phenomenon down to token‑level entropy changes driven by clipping, advantage, token probability and conditional entropy, and introduces STEER, a token‑wise reweighting scheme that stabilizes entropy and yields consistent performance gains on math and code benchmarks.

RLVRReinforcement LearningSTEER
0 likes · 14 min read
Why Large-Model RL Training Narrows Over Time? ACL 2026 Paper Reveals Entropy Collapse
Machine Heart
Machine Heart
Jul 15, 2026 · Artificial Intelligence

Why Does Large‑Model RL Training Narrow? Entropy Insights from ACL Paper

Large‑model reinforcement learning with verifiable rewards often suffers entropy collapse, causing exploration to shrink; this article dissects the phenomenon at the token level, identifies four influencing factors, critiques existing entropy interventions, and introduces STEER—a token‑wise reweighting scheme that stabilizes entropy dynamics and yields consistent gains on math reasoning and coding benchmarks.

LLMRLVRReinforcement Learning
0 likes · 12 min read
Why Does Large‑Model RL Training Narrow? Entropy Insights from ACL Paper