5 papers · 1 filter
ProAS: A Progress-Aware Advantage Shaping Framework for Multi-Turn Agentic Reinforcement Learning
Bowen Ding, Mingda Wan, Yuhan Chen +7
Reinforcement learning (RL) has emerged as the primary paradigm for optimizing Large Language Model (LLM) agents, yet efficient critic-free algorithms (e.g., GRPO) suffer from a fu…
TPPO: Tracking-Value PPO For Language Model Reasoning
Futing Wang, Chenxi Li, Jianhao Yan +6
Proximal Policy Optimization (PPO) is widely used in reinforcement learning with verifiable rewards for language-model reasoning. While \emph{value evolution along trajectory} char…
AutoPruner: Agentic Depth Pruning for Vision-Language-Action Models
Tao Lin, Yikeng Chen, Qiwei Liang +3
Layer pruning reduces the size and inference cost of vision-language-action (VLA) models, but selecting a deployable configuration requires deciding both which layers to remove and…
CacheBridge: Efficient Cross-Model KV Cache Transfer
Xingyu Qu, Siyuan Lu, Zhiyu Chen +2
Sharing context between LLMs in a multi-model system requires the receiving model to prefill the shared prefix because KV caches are model-specific. Recent closed-form cross-model…
Think Longer to Explore Deeper: Learn to Explore In-Context via Length-Incentivized Reinforcement Learning
Futing Wang, Jianhao Yan, Yun Luo +6
Achieving effective test-time scaling requires models to engage in In-Context Exploration -- the intrinsic ability to generate, verify, and refine multiple reasoning hypotheses wit…