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All SourcesarXiv
#training stability

Optimizing Few-Step Generation with Adaptive Matching Distillation

Intermediate
Lichen Bai, Zikai Zhou et al.Feb 7arXiv

Diffusion models make great images and videos but are slow because they usually need many tiny steps.

#diffusion distillation#few-step generation#distribution matching distillation

Self-Hinting Language Models Enhance Reinforcement Learning

Intermediate
Baohao Liao, Hanze Dong et al.Feb 3arXiv

When rewards are rare, a popular training method for language models (GRPO) often stops learning because every try in a group gets the same score, so there is nothing to compare.

#reinforcement learning#GRPO#self-hinting

Turn-PPO: Turn-Level Advantage Estimation with PPO for Improved Multi-Turn RL in Agentic LLMs

Intermediate
Junbo Li, Peng Zhou et al.Dec 18arXiv

Turn-PPO is a new way to train chatty AI agents that act over many steps, by judging each conversation turn as one whole action instead of judging every single token.

#Turn-PPO#multi-turn reinforcement learning#agentic LLMs

Entropy Ratio Clipping as a Soft Global Constraint for Stable Reinforcement Learning

Intermediate
Zhenpeng Su, Leiyu Pan et al.Dec 5arXiv

Reinforcement learning (RL) can make big language models smarter, but off-policy training often pushes updates too far from the “safe zone,” causing unstable learning.

#reinforcement learning#PPO-clip#KL penalty