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KLip-PPO: A per-sample KL perspective on PPO-Clip

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arXiv:2606. 23932v1 Announce Type: new Abstract: Proximal Policy Optimization (PPO) is the standard policy-gradient algorithm for on-policy reinforcement learning.

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Authors: Riccardo Colletti, Robin Holzinger
Released: 2026-06-24 (15d ago)
Topic: Related to current project
Relation to my work: Read next because KLip-PPO: A per-sample KL perspective on PPO-Clip overlaps with clean result "Language-mismatch LoRA SFT on Qwen2.5-7B leaks the trained completion language into bystander directives the model was never trained on, absent under same-language SFT (LOW confidence)", clean result "Training one persona to emit a [ZLT] marker without bystanders adopting it has a one-cell-wide LR x epochs window on Qwen2.5-7B-Instruct (LOW confidence)", clean result "A pretraining-data-poisoned Qwen3-4B backdoor only fires on the exact trigger tokens — paraphrases don't activate it, and base-model similarity to the trigger doesn't predict which inputs fire (MODERATE confidence)". Matching terms: rect, rate, implement, compare, control, follow-up, on-policy. Source: arxiv cs.LG (Machine Learning).
arXiv: 2606.23932

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