4 papers
Practical Online KV Cache Compaction for LLM Agents: An Empirical Study
Yujian Liu, Jiabao Ji, Li An +4
LLM agents accumulate long trajectories of reasoning steps, tool calls, and environment feedback, making the KV cache a major inference bottleneck. KV cache compaction can reduce t…
Speculate While You Reason: Teaching Agents to Predict Their Next Tool Call via Joint Agent-Speculator RL
Jiabao Ji, Yujian Liu, Li An +4
Large language model agents often spend substantial wall-clock time waiting for tool call results. Tool-call speculation can hide this latency by predicting and pre-executing an ag…
Agentic Reinforcement Learning for Real-World Code Repair
Siyu Zhu, Anastasiya Karpovich, Albert Chen +6
We tackle the challenge of training reliable code-fixing agents in real repositories, where complex builds and shifting dependencies make evaluation unstable. We developed a verifi…
Planner-R1: Reward Shaping Enables Efficient Agentic RL with Smaller LLMs
Siyu Zhu, Yanbin Jiang, Hejian Sang +6
We investigated Agentic RL with large language models on the \textsc{TravelPlanner} benchmark. Our approach, \textsc{Planner-R1}, achieved a \textbf{56.9\%} final-pass rate with on…