Yu Li

The work proposes Comparative Inference for Tool-use Agents (CITA), which trains a Comparative Inference Model (CIM) from paired signals combining observed tool behavior, scalable supervision from a Bayesian tool-graph simulator, and semantic judgments from LLM-based comparison, so that the model estimates how likely a possible next tool invocation is to support final task success under the current context; across three tool-use benchmarks and multiple backbone LLMs, CITA consistently improves Tool F1 and task success, and additional analysis shows CIM learns accurate step-level value estimates for comparative tool choices.

The work proposes Comparative Inference for Tool-use Agents (CITA), which trains a Comparative Inference Model (CIM) from paired signals combining observed tool behavior, scalable supervision from a Bayesian tool-graph simulator, and semantic judgments from LLM-based comparison, so that the model estimates how likely a possible next tool invocation is to support final task success under the current context; across three tool-use benchmarks and multiple backbone LLMs, CITA consistently improves Tool F1 and task success, and additional analysis shows CIM learns accurate step-level value estimates for comparative tool choices.

The work proposes CIPO: it first mines executable composite skills from successful tool-use trajectories via budget-constrained BPE-style merging, then augments GRPO by branching at the first eligible granularity decision of each base rollout and using the paired outcome difference as a bounded supplementary reward; across TOOLATHLON, TOUCAN, and TRAJECT-Bench with Qwen2.5-7B and Llama3.1-8B, CIPO attains the highest Tool F1 and TSR in all six settings with an average Comp./Raw of 0.845 versus 0.912 for GRPO + Skills, and the gain does not come from simply invoking skills more often.

The work proposes CIPO: it first mines executable composite skills from successful tool-use trajectories via budget-constrained BPE-style merging, then augments GRPO by branching at the first eligible granularity decision of each base rollout and using the paired outcome difference as a bounded supplementary reward; across TOOLATHLON, TOUCAN, and TRAJECT-Bench with Qwen2.5-7B and Llama3.1-8B, CIPO attains the highest Tool F1 and TSR in all six settings with an average Comp./Raw of 0.845 versus 0.912 for GRPO + Skills, and the gain does not come from simply invoking skills more often.