Robust Tool Use via Fission-GRPO: Learning to Recover from Execution Errors
Large language models (LLMs) can call tools effectively, yet they remain brittle in multi-turn execution: following a tool call error, smaller models often degenerate into repetitive invalid re-invocations, failing to interpret error feedback and self-correct. This brittleness hinders reliable real-world deployment, where the execution errors are inherently inevitable during tool interaction procedures. We identify a key limitation of current approaches: standard reinforcement learning (RL) treats errors as sparse negative rewards, providing no guidance on how to recover, while pre-collected synthetic error-correction datasets suffer from distribution mismatch with the model's on-policy error modes. To bridge this gap, we propose Fission-GRPO, a framework that converts execution errors into corrective supervision within the RL training loop. Our core mechanism fissions each failed trajectory into a new training instance by augmenting it with diagnostic feedback from a finetuned Error Simulator, then resampling recovery rollouts on-policy. This enables the model to learn from the precise errors it makes during exploration, rather than from static, pre-collected error cases. On the BFCL v4 Multi-Turn, Fission-GRPO improves the error recovery rate of Qwen3-8B by 5.7% absolute, crucially, yielding a 4% overall accuracy gain (42.75% to 46.75%) over GRPO and outperforming specialized tool-use agents.
698042726676f93322706688
Zhiwei Zhang
698042726676f93322706689
Fei Zhao
claimed_verified
2026-02-02T16:52:17.466Z
65328aa39326d6da5ff19b52
/avatars/5c3de984cd6eba69616bb608796865c5.svg
Fei Zhao
Hiiamein
user
Hiiamein
698042726676f9332270668a
Rui Wang
698042726676f9332270668b
Zezhong Wang
698042726676f9332270668c
Bin Liang
698042726676f9332270668d
Jiakang Wang
698042726676f9332270668e
Yao Hu
698042726676f9332270668f
Shaosheng Cao
698042726676f93322706690
Kam-Fai Wong
Robust Tool Use via Fission-GRPO: Learning to Recover from Execution Errors
Large language models (LLMs) can call tools effectively, yet they remain brittle in multi-turn execution: following a tool call error, smaller models often degenerate into repetitive invalid re-invocations, failing to interpret error feedback and self-correct. This brittleness hinders reliable real-world deployment, where the execution errors are inherently inevitable during tool interaction procedures. We identify a key limitation of current approaches: standard reinforcement learning (RL) treats errors as sparse negative rewards, providing no guidance on how to recover, while pre-collected synthetic error-correction datasets suffer from distribution mismatch with the model's on-policy error modes. To bridge this gap, we propose Fission-GRPO, a framework that converts execution errors into corrective supervision within the RL training loop. Our core mechanism fissions each failed trajectory into a new training instance by augmenting it with diagnostic feedback from a finetuned Error Simulator, then resampling recovery rollouts on-policy. This enables the model to learn from the precise errors it makes during exploration, rather than from static, pre-collected error cases. On the BFCL v4 Multi-Turn, Fission-GRPO improves the error recovery rate of Qwen3-8B by 5.7% absolute, crucially, yielding a 4% overall accuracy gain (42.75% to 46.75%) over GRPO and outperforming specialized tool-use agents.
A framework called Fission-GRPO is introduced to improve multi-turn tool execution in large language models by converting execution errors into corrective supervision during reinforcement learning training.
large language models tool calling reinforcement learning error recovery Fission-GRPO GRPO Error Simulator on-policy sampling multi-turn execution trajectory fission