The Flow has not summarised this story yet — read it at Hugging Face Trending Papers.
EPIC introduces a new PPO-style clipping method that operates at the prefix level rather than token level, using the exact reverse-KL coordinate to regulate policy drift during autoregressive generation. By reformulating generation as a finite-horizon Markov decision process, the authors derive a performance-difference identity that identifies the cumulative likelihood ratio of the generated prefix as the exact importance weight for each policy-improvement step. Experiments on off‑policy reinforcement learning with a sparse mixture‑of‑experts language model demonstrate that EPIC consistently outperforms standard and sequence‑level clipping baselines on mathematical‑reasoning benchmarks while remaining competitive with routing‑replay alternatives.
arXiv:2602. 04879v3 Announce Type: replace-cross Abstract: Reinforcement learning (RL) has become a cornerstone for fine-tuning Large Language Models (LLMs), with Proximal Policy Optimization (PPO) serving as the de facto standard algorithm.
arXiv:2606. 09821v1 Announce Type: new Abstract: Reinforcement learning (RL) has become a key component of post-training large language models (LLMs).
arXiv:2606. 10968v1 Announce Type: cross Abstract: Reinforcement learning with verifiable rewards (RLVR) has become standard for improving LLM reasoning.
arXiv:2606. 31575v1 Announce Type: new Abstract: Reinforcement learning (RL) has become a powerful tool for propelling Large Language Models (LLMs) beyond imitation-based training towards more robust reasoning capabilities.
arXiv:2607. 10848v1 Announce Type: new Abstract: Reinforcement learning for large language models (LLMs) typically relies on trust-region masks to stabilize off-policy updates.