6 papers
Learning Intractable Multimodal Policies with Reparameterization and Diversity Regularization
Ziqi Wang, Jiashun Liu, Ling Pan
Traditional continuous deep reinforcement learning (RL) algorithms employ deterministic or unimodal Gaussian actors, which cannot express complex multimodal decision distributions.…
Asymmetric Proximal Policy Optimization: mini-critics boost LLM reasoning
Jiashun Liu, Johan Obando-Ceron, Han Lu +7
Most recent RL for LLMs (RL4LLM) methods avoid explicit critics, replacing them with average advantage baselines. This shift is largely pragmatic: conventional value functions are…
Part II: ROLL Flash -- Accelerating RLVR and Agentic Training with Asynchrony
Han Lu, Zichen Liu, Shaopan Xiong +19
Synchronous Reinforcement Learning (RL) post-training has emerged as a crucial step for enhancing Large Language Models (LLMs) with diverse capabilities. However, many systems desi…
Part I: Tricks or Traps? A Deep Dive into RL for LLM Reasoning
Zihe Liu, Jiashun Liu, Yancheng He +13
Reinforcement learning for LLM reasoning has rapidly emerged as a prominent research area, marked by a significant surge in related studies on both algorithmic innovations and prac…
The Courage to Stop: Overcoming Sunk Cost Fallacy in Deep Reinforcement Learning
Jiashun Liu, Johan Obando-Ceron, Pablo Samuel Castro +2
Off-policy deep reinforcement learning (RL) typically leverages replay buffers for reusing past experiences during learning. This can help improve sample efficiency when the collec…
Measure gradients, not activations! Enhancing neuronal activity in deep reinforcement learning
Jiashun Liu, Zihao Wu, Johan Obando-Ceron +3
Deep reinforcement learning (RL) agents frequently suffer from neuronal activity loss, which impairs their ability to adapt to new data and learn continually. A common method to qu…