Synthesize and Reward -- Reinforcement Learning for Multi-Step Tool Use in Live Environments 事件

Synthesize and Reward -- Reinforcement Learning for Multi-Step Tool Use in Live Environments arXiv:2606.03892v1 Announce Type: new Abstract: Training LLMs to orchestrate multi-step tool calls is held back by three coupled obstacles: realistic stateful execution environments are costly to build, synthetic training queries are often detached from the server's actual state (so the generated tool calls fail to execute), and recall-based RL rewards incentivize verbose tool-calling patterns. We prese