New LLM RL paper says PPO-Clip hurts exploration and RIPO lifts AIME24 by 60%
burny_tech · x · 2026-07-25
A new paper argues that PPO-Clip in LLM reinforcement learning has a geometric flaw: it measures policy change in Euclidean terms even though policy updates live on a Riemannian manifold.
The authors say this mismatch suppresses rare but useful actions, causing exploration collapse. Their fix, Riemannian Isometric Policy Optimization (RIPO), gives low-probability actions more room and high-probability actions less, improving stability and reportedly boosting AIME24 performance by up to 60% over GRPO across seven benchmarks.
Related event: RIPO Overcomes PPO-Clip's Exploration Collapse in LLM RL(2 posts)→
More from Research
- Could 10k agents discover learning methods beyond backprop, or just tweak existing ones? — SeunghyunSEO7 · 2026-09-11
- Cognition's SWE-2 uses a KKT duality argument in RL to shift the effort Pareto curve — YouJiacheng · 2026-09-11
- VidMap uses RoMa coarse matching on all frames, fine-scale only for keyframes — ducha_aiki · 2026-09-11
- Bug Hunt Bench author: leaderboard noise is about 2-3 points — PawelHuryn · 2026-09-11
- PNAS paper shows a tiny billiard-ball system is a universal computer — undecidability lives in two dimensions — eigensteve · 2026-09-11
- New paper: Absolute pose estimation from affine cues and gravity direction — ducha_aiki · 2026-09-11