@inproceedings{li-etal-2026-skill-cdpo,
title = "Skill-{CDPO}: Evolving Agent Tool-Use via Critical Step Preference Optimization",
author = "Li, Yuchen and
Lin, Jinghao and
Wang, Yuanqing",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EMNLP} 2026",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-emnlp.1985/",
pages = "42309--42326",
ISBN = "979-8-89176-462-0",
abstract = "Compact open-source language models lag behind their larger counterparts in agentic tool-use reliability, yet standard remedies face fundamental obstacles: supervised fine-tuning suffers from exposure bias, while reinforcement learning is hampered by sparse credit assignment over long tool-interaction trajectories. We introduce Skill-CDPO, a progressive framework that first acquires tool-use skills at inference time through static tool analysis and dynamic strategy refinement, then distills the resulting error-correction signals into parameter updates via Critical Step DPO (CDPO). CDPO identifies the specific trajectory steps where model capability is the bottleneck{---}through rollout divergence between a local policy and an expert model{---}and constructs distributional preference pairs from all cross-group rollouts at those steps, weighted by both step-level criticality and pair-level score gaps. This provides dense, fine-grained supervision without requiring a process reward model. We evaluate Skill-CDPO on three medical agent benchmarks{---}PubMed Search (a new PubMed-based deep research benchmark we contribute), CureBench, and MedBrowseComp{---}using an 8B-parameter deep research model. Skill-CDPO substantially outperforms SFT and trajectory-level DPO baselines and achieves competitive or superior performance compared to GPT-5.2 on retrieval-intensive tasks. Our code and data are available at https://anonymous.4open.science/r/Skill-CDPO-BF51."
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<abstract>Compact open-source language models lag behind their larger counterparts in agentic tool-use reliability, yet standard remedies face fundamental obstacles: supervised fine-tuning suffers from exposure bias, while reinforcement learning is hampered by sparse credit assignment over long tool-interaction trajectories. We introduce Skill-CDPO, a progressive framework that first acquires tool-use skills at inference time through static tool analysis and dynamic strategy refinement, then distills the resulting error-correction signals into parameter updates via Critical Step DPO (CDPO). CDPO identifies the specific trajectory steps where model capability is the bottleneck—through rollout divergence between a local policy and an expert model—and constructs distributional preference pairs from all cross-group rollouts at those steps, weighted by both step-level criticality and pair-level score gaps. This provides dense, fine-grained supervision without requiring a process reward model. We evaluate Skill-CDPO on three medical agent benchmarks—PubMed Search (a new PubMed-based deep research benchmark we contribute), CureBench, and MedBrowseComp—using an 8B-parameter deep research model. Skill-CDPO substantially outperforms SFT and trajectory-level DPO baselines and achieves competitive or superior performance compared to GPT-5.2 on retrieval-intensive tasks. Our code and data are available at https://anonymous.4open.science/r/Skill-CDPO-BF51.</abstract>
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%0 Conference Proceedings
%T Skill-CDPO: Evolving Agent Tool-Use via Critical Step Preference Optimization
%A Li, Yuchen
%A Lin, Jinghao
%A Wang, Yuanqing
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F li-etal-2026-skill-cdpo
%X Compact open-source language models lag behind their larger counterparts in agentic tool-use reliability, yet standard remedies face fundamental obstacles: supervised fine-tuning suffers from exposure bias, while reinforcement learning is hampered by sparse credit assignment over long tool-interaction trajectories. We introduce Skill-CDPO, a progressive framework that first acquires tool-use skills at inference time through static tool analysis and dynamic strategy refinement, then distills the resulting error-correction signals into parameter updates via Critical Step DPO (CDPO). CDPO identifies the specific trajectory steps where model capability is the bottleneck—through rollout divergence between a local policy and an expert model—and constructs distributional preference pairs from all cross-group rollouts at those steps, weighted by both step-level criticality and pair-level score gaps. This provides dense, fine-grained supervision without requiring a process reward model. We evaluate Skill-CDPO on three medical agent benchmarks—PubMed Search (a new PubMed-based deep research benchmark we contribute), CureBench, and MedBrowseComp—using an 8B-parameter deep research model. Skill-CDPO substantially outperforms SFT and trajectory-level DPO baselines and achieves competitive or superior performance compared to GPT-5.2 on retrieval-intensive tasks. Our code and data are available at https://anonymous.4open.science/r/Skill-CDPO-BF51.
%U https://aclanthology.org/2026.findings-emnlp.1985/
%P 42309-42326
Markdown (Informal)
[Skill-CDPO: Evolving Agent Tool-Use via Critical Step Preference Optimization](https://aclanthology.org/2026.findings-emnlp.1985/) (Li et al., Findings 2026)
ACL