Papers

Filtered to manifold learning · clear filter

Browse by term

continual learning 64reinforcement learning 34large language models 17benchmarking 12vision-language models 10generative models 8language models 8video generation 7multimodal models 6natural language processing 6robotics 6world models 6benchmarks 5diffusion models 5on-policy distillation 5policy optimization 5scalability 5self-distillation 5vision-language-action models 5autoregressive models 4computer vision 4diffusion transformers 4LLMs 4multimodal large language models 4verifiable rewards 4attention mechanisms 3embodied intelligence 3image editing 3long-term memory 3multimodal learning 3

Matching papers

Beyond Euclidean Clipping: Overcoming Exploration Collapse in LLM RL via Riemannian Isometric Policy Optimization

13 upvotes · 11 JUL 2026 · Zhicheng Cai, Xinyuan Guo, Hanlin Wu et al.

This paper addresses a problem in reinforcement learning (RL) where algorithms with PPO-Clip suffer from "exploration collapse", where they fail to explore new areas of the environment. The authors propose a new method called Riemannian Isometric Policy Optimization (RIPO) that helps to balance exploration and exploitation by using a more accurate geometric metric.

reinforcement learningpolicy optimizationRiemannian geometrymanifold learningdeep reinforcement learning