Paper Citation Record · LEDGER

Agentic-DPO: From Imitation to Agentic Policy Optimization on Expert Trajectories

As of 21 August 2026, this Paper Citation Record lists 58 of 58 outbound references and 2 inbound Pith citation observations for arXiv:2607.10601.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2607.10601 v1

Coverage vector

measured 58 of 58 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-07-14T10:33:54.851493Z

measured 60 of 60 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-20T06:33:59.587034+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-04T09:44:31.301557Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: pith, observed 2026-08-04T12:16:13.904932Z

Reference resolution

58 of 58 outbound references displayed

verified exact0
verified fuzzy0
unresolved58
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 4bd8000a-17c5-497e-991b-75af1d98781d · outbound

This paper cites On-policy distillation of language models: Learning from self-generated mistakes.

Agentic-DPO: From Imitation to Agentic Policy Optimization on Expert Trajectories On-policy distillation of language models: Learning from self-generated mistakes

Reference 1

Resolution: unresolved; no resolver link, observed 2026-07-14T10:33:54.851493Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7bed126f-49a0-4b34-8771-8c9097274e48 · outbound

This paper cites A general theoretical paradigm to understand learning from human preferences.

Agentic-DPO: From Imitation to Agentic Policy Optimization on Expert Trajectories A general theoretical paradigm to understand learning from human preferences

Reference 2

Resolution: unresolved; no resolver link, observed 2026-07-14T10:33:54.851493Z

Source-reported events for the cited work

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Observation 2a742f75-8987-4797-96ca-d4981f57b7da · outbound

This paper cites Digirl: Training in-the-wild device-control agents with autonomous reinforcement learning.

Agentic-DPO: From Imitation to Agentic Policy Optimization on Expert Trajectories Digirl: Training in-the-wild device-control agents with autonomous reinforcement learning

Reference 3

Resolution: unresolved; no resolver link, observed 2026-07-14T10:33:54.851493Z

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Observation fadb3a24-6dea-449d-8890-a67f0292a037 · outbound

This paper cites FireAct: Toward Language Agent Fine-tuning.

Agentic-DPO: From Imitation to Agentic Policy Optimization on Expert Trajectories FireAct: Toward Language Agent Fine-tuning

Reference 4

Resolution: unresolved; no resolver link, observed 2026-07-14T10:33:54.851493Z

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Observation aa1023e0-2103-4140-8bcf-38a7d6edef7f · outbound

This paper cites Agent-FLAN: Designing Data and Methods of Effective Agent Tuning for Large Language Models.

Agentic-DPO: From Imitation to Agentic Policy Optimization on Expert Trajectories Agent-FLAN: Designing Data and Methods of Effective Agent Tuning for Large Language Models

Reference 5

Resolution: unresolved; no resolver link, observed 2026-07-14T10:33:54.851493Z

Source-reported events for the cited work

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Observation a2032254-9900-4d09-a479-56845208ee84 · outbound

This paper cites ATLaS: Agent Tuning via Learning Critical Steps.

Agentic-DPO: From Imitation to Agentic Policy Optimization on Expert Trajectories ATLaS: Agent Tuning via Learning Critical Steps

Reference 6

Resolution: unresolved; no resolver link, observed 2026-07-14T10:33:54.851493Z

Source-reported events for the cited work

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Observation 073b40c5-dbb9-486f-9b6b-5a46a17d6e3f · outbound

This paper cites Self-play fine-tuning converts weak language models to strong language models.

Agentic-DPO: From Imitation to Agentic Policy Optimization on Expert Trajectories Self-play fine-tuning converts weak language models to strong language models

Reference 7

Resolution: unresolved; no resolver link, observed 2026-07-14T10:33:54.851493Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1b629639-8ed1-4636-8dfb-7d7e727e1127 · outbound

This paper cites Rethinking DPO: The Role of Rejected Responses in Preference Misalignment.

Agentic-DPO: From Imitation to Agentic Policy Optimization on Expert Trajectories Rethinking DPO: The Role of Rejected Responses in Preference Misalignment

Reference 8

Resolution: unresolved; no resolver link, observed 2026-07-14T10:33:54.851493Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation daced041-f2dc-4f63-8d6b-cc3dd8ae344b · outbound

This paper cites Agent-RLVR: Training Software Engineering Agents via Guidance and Environment Rewards.

Agentic-DPO: From Imitation to Agentic Policy Optimization on Expert Trajectories Agent-RLVR: Training Software Engineering Agents via Guidance and Environment Rewards

Reference 9

Resolution: unresolved; no resolver link, observed 2026-07-14T10:33:54.851493Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0aabe613-5e49-464b-bcd6-d37bf6f05257 · outbound

This paper cites Mind2Web: Towards a generalist agent for the web.

Agentic-DPO: From Imitation to Agentic Policy Optimization on Expert Trajectories Mind2Web: Towards a generalist agent for the web

Reference 10

Resolution: unresolved; no resolver link, observed 2026-07-14T10:33:54.851493Z

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Observation eea484d1-9b2d-40cf-947d-18c3272a465f · outbound

This paper cites KTO: Model Alignment as Prospect Theoretic Optimization.

Agentic-DPO: From Imitation to Agentic Policy Optimization on Expert Trajectories KTO: Model Alignment as Prospect Theoretic Optimization

Reference 11

Resolution: unresolved; no resolver link, observed 2026-07-14T10:33:54.851493Z

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Observation ddddbc5f-1585-4c36-8a98-841c2807c6af · outbound

This paper cites AgentRefine: Enhancing Agent Generalization through Refinement Tuning.

Agentic-DPO: From Imitation to Agentic Policy Optimization on Expert Trajectories AgentRefine: Enhancing Agent Generalization through Refinement Tuning

Reference 12

Resolution: unresolved; no resolver link, observed 2026-07-14T10:33:54.851493Z

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Observation ea5c0a9f-0bad-41e1-848d-3d75d73784c8 · outbound

This paper cites Solving the granularity mismatch: Hierarchical preference learning for long-horizon llm agents.

Agentic-DPO: From Imitation to Agentic Policy Optimization on Expert Trajectories Solving the granularity mismatch: Hierarchical preference learning for long-horizon llm agents

Reference 13

Resolution: unresolved; no resolver link, observed 2026-07-14T10:33:54.851493Z

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Observation ecceda03-3c18-4ebb-bdfb-f024a19b2c26 · outbound

This paper cites Gemma 3 Technical Report.

Agentic-DPO: From Imitation to Agentic Policy Optimization on Expert Trajectories Gemma 3 Technical Report

Reference 14

Resolution: unresolved; no resolver link, observed 2026-07-14T10:33:54.851493Z

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Observation 13b04911-4930-4a14-b996-e942cbefe43b · outbound

This paper cites StableToolBench: Towards Stable Large-Scale Benchmarking on Tool Learning of Large Language Models.

Agentic-DPO: From Imitation to Agentic Policy Optimization on Expert Trajectories StableToolBench: Towards Stable Large-Scale Benchmarking on Tool Learning of Large Language Models

Reference 15

Resolution: unresolved; no resolver link, observed 2026-07-14T10:33:54.851493Z

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Observation 1323640d-a83f-4a73-99fe-49b9b9ef3352 · outbound

This paper cites SWE-bench: Can Language Models Resolve Real-World GitHub Issues?.

Agentic-DPO: From Imitation to Agentic Policy Optimization on Expert Trajectories SWE-bench: Can Language Models Resolve Real-World GitHub Issues?

Reference 16

Resolution: unresolved; no resolver link, observed 2026-07-14T10:33:54.851493Z

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Observation 95c6e5d8-ecde-4168-a827-3ea7abf5ff01 · outbound

This paper cites DiaTool-DPO: Multi-Turn Direct Preference Optimization for Tool-Augmented Large Language Models.

Agentic-DPO: From Imitation to Agentic Policy Optimization on Expert Trajectories DiaTool-DPO: Multi-Turn Direct Preference Optimization for Tool-Augmented Large Language Models

Reference 17

Resolution: unresolved; no resolver link, observed 2026-07-14T10:33:54.851493Z

Source-reported events for the cited work

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Observation 87b2b2cf-224f-4ae4-9ad6-dde87fe12866 · outbound

This paper cites Step-DPO: Step-wise Preference Optimization for Long-chain Reasoning of LLMs.

Agentic-DPO: From Imitation to Agentic Policy Optimization on Expert Trajectories Step-DPO: Step-wise Preference Optimization for Long-chain Reasoning of LLMs

Reference 18

Resolution: unresolved; no resolver link, observed 2026-07-14T10:33:54.851493Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 39980b5c-efff-4ecf-817c-a9bd5c061323 · outbound

This paper cites RLAIF vs. RLHF: Scaling Reinforcement Learning from Human Feedback with AI Feedback.

Agentic-DPO: From Imitation to Agentic Policy Optimization on Expert Trajectories RLAIF vs. RLHF: Scaling Reinforcement Learning from Human Feedback with AI Feedback

Reference 19

Resolution: unresolved; no resolver link, observed 2026-07-14T10:33:54.851493Z

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Unavailable: canonical work link unavailable.

Observation 7f9fcd0f-ff5f-4d3d-905f-9611cafbb6d2 · outbound

This paper cites Hammer: Robust function- calling for on-device language models via function masking.arXiv preprint arXiv:2410.04587, 2024.

Agentic-DPO: From Imitation to Agentic Policy Optimization on Expert Trajectories Hammer: Robust function- calling for on-device language models via function masking.arXiv preprint arXiv:2410.04587, 2024

Reference 20

Resolution: unresolved; no resolver link, observed 2026-07-14T10:33:54.851493Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5cd85973-f39f-4e1c-88f6-0945f4952fda · outbound

This paper cites ToolACE: Winning the Points of LLM Function Calling.

Agentic-DPO: From Imitation to Agentic Policy Optimization on Expert Trajectories ToolACE: Winning the Points of LLM Function Calling

Reference 21

Resolution: unresolved; no resolver link, observed 2026-07-14T10:33:54.851493Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5eea86bb-7289-4a91-95de-a03bb0a7515f · outbound

This paper cites Simpo: Simple preference optimization with a reference-free reward.Advances in Neural Information Processing Systems, 37:124198–124235, 2024.

Agentic-DPO: From Imitation to Agentic Policy Optimization on Expert Trajectories Simpo: Simple preference optimization with a reference-free reward.Advances in Neural Information Processing Systems, 37:124198–124235, 2024

Reference 22

Resolution: unresolved; no resolver link, observed 2026-07-14T10:33:54.851493Z

Source-reported events for the cited work

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Observation 79571b25-a819-4894-a3e3-928ce0f60229 · outbound

This paper cites Gui agents: A survey.

Agentic-DPO: From Imitation to Agentic Policy Optimization on Expert Trajectories Gui agents: A survey

Reference 23

Resolution: unresolved; no resolver link, observed 2026-07-14T10:33:54.851493Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ce7beb51-7129-4ea0-883d-91ca30119bf8 · outbound

This paper cites Training language models to follow instructions with human feedback.

Agentic-DPO: From Imitation to Agentic Policy Optimization on Expert Trajectories Training language models to follow instructions with human feedback

Reference 24

Resolution: unresolved; no resolver link, observed 2026-07-14T10:33:54.851493Z

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Unavailable: canonical work link unavailable.

Observation 2e5ed80b-ce8e-440d-ac78-7c1869bd53f5 · outbound

This paper cites Smaug: Fixing Failure Modes of Preference Optimisation with DPO-Positive.

Agentic-DPO: From Imitation to Agentic Policy Optimization on Expert Trajectories Smaug: Fixing Failure Modes of Preference Optimisation with DPO-Positive

Reference 25

Resolution: unresolved; no resolver link, observed 2026-07-14T10:33:54.851493Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bfb841ce-f1d5-482e-a2b8-b447e2059dbb · outbound

This paper cites Patil, Huanzhi Mao, Fanjia Yan, Charlie Cheng-Jie Ji, Vishnu Suresh, Ion Stoica, and Joseph E.

Agentic-DPO: From Imitation to Agentic Policy Optimization on Expert Trajectories Patil, Huanzhi Mao, Fanjia Yan, Charlie Cheng-Jie Ji, Vishnu Suresh, Ion Stoica, and Joseph E

Reference 26

Resolution: unresolved; no resolver link, observed 2026-07-14T10:33:54.851493Z

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Observation 18440b04-b9a5-4dea-b32d-f6f4e07bcc55 · outbound

This paper cites WebRL: Training LLM Web Agents via Self-Evolving Online Curriculum Reinforcement Learning.

Agentic-DPO: From Imitation to Agentic Policy Optimization on Expert Trajectories WebRL: Training LLM Web Agents via Self-Evolving Online Curriculum Reinforcement Learning

Reference 27

Resolution: unresolved; no resolver link, observed 2026-07-14T10:33:54.851493Z

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Observation cac12049-04b6-44bc-9e63-d089bc29f8c2 · outbound

This paper cites ToolLLM: Facilitating Large Language Models to Master 16000+ Real-world APIs.

Agentic-DPO: From Imitation to Agentic Policy Optimization on Expert Trajectories ToolLLM: Facilitating Large Language Models to Master 16000+ Real-world APIs

Reference 28

Resolution: unresolved; no resolver link, observed 2026-07-14T10:33:54.851493Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8a548b2e-ffed-4753-9dc6-204b79192997 · outbound

This paper cites Direct preference optimization: Your language model is secretly a reward model.

Agentic-DPO: From Imitation to Agentic Policy Optimization on Expert Trajectories Direct preference optimization: Your language model is secretly a reward model

Reference 29

Resolution: unresolved; no resolver link, observed 2026-07-14T10:33:54.851493Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0c8dbf53-6548-4bfb-970e-1b80295858fd · outbound

This paper cites Proximal Policy Optimization Algorithms.

Agentic-DPO: From Imitation to Agentic Policy Optimization on Expert Trajectories Proximal Policy Optimization Algorithms

Reference 30

Resolution: unresolved; no resolver link, observed 2026-07-14T10:33:54.851493Z

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Unavailable: canonical work link unavailable.

Observation 7144fbd5-b679-453c-8af3-4f5464f345c6 · outbound

This paper cites DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models.

Agentic-DPO: From Imitation to Agentic Policy Optimization on Expert Trajectories DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models

Reference 31

Resolution: unresolved; no resolver link, observed 2026-07-14T10:33:54.851493Z

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Observation 61d49fa2-1d15-422e-80b3-39416bc77069 · outbound

This paper cites ScribeAgent: Towards Specialized Web Agents Using Production-Scale Workflow Data.

Agentic-DPO: From Imitation to Agentic Policy Optimization on Expert Trajectories ScribeAgent: Towards Specialized Web Agents Using Production-Scale Workflow Data

Reference 32

Resolution: unresolved; no resolver link, observed 2026-07-14T10:33:54.851493Z

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Observation 2dbc19d7-538f-4172-8aea-d9e62cf42b6d · outbound

This paper cites Direct Multi-Turn Preference Optimization for Language Agents.

Agentic-DPO: From Imitation to Agentic Policy Optimization on Expert Trajectories Direct Multi-Turn Preference Optimization for Language Agents

Reference 33

Resolution: unresolved; no resolver link, observed 2026-07-14T10:33:54.851493Z

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Observation a648e3d0-d204-426e-9c40-b3c0df0fe317 · outbound

This paper cites Reflexion: Language agents with verbal reinforcement learning.Advances in neural information processing systems, 36:8634–8652, 2023.

Agentic-DPO: From Imitation to Agentic Policy Optimization on Expert Trajectories Reflexion: Language agents with verbal reinforcement learning.Advances in neural information processing systems, 36:8634–8652, 2023

Reference 34

Resolution: unresolved; no resolver link, observed 2026-07-14T10:33:54.851493Z

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Observation 7fd8d7a3-0a9a-4b3a-8383-414f080a6a23 · outbound

This paper cites ALFWorld: Aligning Text and Embodied Environments for Interactive Learning.

Agentic-DPO: From Imitation to Agentic Policy Optimization on Expert Trajectories ALFWorld: Aligning Text and Embodied Environments for Interactive Learning

Reference 35

Resolution: unresolved; no resolver link, observed 2026-07-14T10:33:54.851493Z

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Observation 9e11918f-c40e-4fcf-9e2c-4f56c9e8a377 · outbound

This paper cites Trial and Error: Exploration-Based Trajectory Optimization for LLM Agents.

Agentic-DPO: From Imitation to Agentic Policy Optimization on Expert Trajectories Trial and Error: Exploration-Based Trajectory Optimization for LLM Agents

Reference 36

Resolution: unresolved; no resolver link, observed 2026-07-14T10:33:54.851493Z

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Observation dc48e6f6-ed47-47ba-a876-ac2299bc4867 · outbound

This paper cites Swe-lego: Pushing the limits of supervised fine-tuning for software issue resolving.arXiv preprint arXiv:2601.01426, 2026.

Agentic-DPO: From Imitation to Agentic Policy Optimization on Expert Trajectories Swe-lego: Pushing the limits of supervised fine-tuning for software issue resolving.arXiv preprint arXiv:2601.01426, 2026

Reference 37

Resolution: unresolved; no resolver link, observed 2026-07-14T10:33:54.851493Z

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Observation eddeb439-8fc2-4d3c-bff0-b692f58a41f7 · outbound

This paper cites Triplets better than pairs: Towards stable and effective self-play fine-tuning for LLMs.

Agentic-DPO: From Imitation to Agentic Policy Optimization on Expert Trajectories Triplets better than pairs: Towards stable and effective self-play fine-tuning for LLMs

Reference 38

Resolution: unresolved; no resolver link, observed 2026-07-14T10:33:54.851493Z

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Observation 072b71ac-60e3-40ac-b0ad-f30414dc02fe · outbound

This paper cites RAGEN: Understanding Self-Evolution in LLM Agents via Multi-Turn Reinforcement Learning.

Agentic-DPO: From Imitation to Agentic Policy Optimization on Expert Trajectories RAGEN: Understanding Self-Evolution in LLM Agents via Multi-Turn Reinforcement Learning

Reference 39

Resolution: unresolved; no resolver link, observed 2026-07-14T10:33:54.851493Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d66964fa-2af2-4a5d-a84c-76b7f7c114ef · outbound

This paper cites Webagent-r1: Training web agents via end-to-end multi-turn reinforcement learning.

Agentic-DPO: From Imitation to Agentic Policy Optimization on Expert Trajectories Webagent-r1: Training web agents via end-to-end multi-turn reinforcement learning

Reference 40

Resolution: unresolved; no resolver link, observed 2026-07-14T10:33:54.851493Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bbcf4ede-7dad-48e6-b29b-dd7363a87829 · outbound

This paper cites On the generalization of SFT: A reinforcement learning perspective with reward rectification.arXiv preprint arXiv:2508.05629, 2025.

Agentic-DPO: From Imitation to Agentic Policy Optimization on Expert Trajectories On the generalization of SFT: A reinforcement learning perspective with reward rectification.arXiv preprint arXiv:2508.05629, 2025

Reference 41

Resolution: unresolved; no resolver link, observed 2026-07-14T10:33:54.851493Z

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Unavailable: canonical work link unavailable.

Observation e80a9c3c-7e51-4714-8bcb-d02e0649a8c2 · outbound

This paper cites an unresolved cited work.

Agentic-DPO: From Imitation to Agentic Policy Optimization on Expert Trajectories Unresolved cited work

Reference 42

Resolution: unresolved; no resolver link, observed 2026-07-14T10:33:54.851493Z

Source-reported events for the cited work

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Observation 6b1e739c-bb86-4664-9402-992f048bad50 · outbound

This paper cites AgentGym-RL: Training LLM Agents for Long-Horizon Decision Making through Multi-Turn Reinforcement Learning.

Agentic-DPO: From Imitation to Agentic Policy Optimization on Expert Trajectories AgentGym-RL: Training LLM Agents for Long-Horizon Decision Making through Multi-Turn Reinforcement Learning

Reference 43

Resolution: unresolved; no resolver link, observed 2026-07-14T10:33:54.851493Z

Source-reported events for the cited work

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Observation f322476f-585f-4aa2-acac-e6eca0458269 · outbound

This paper cites Qwen3 Technical Report.

Agentic-DPO: From Imitation to Agentic Policy Optimization on Expert Trajectories Qwen3 Technical Report

Reference 44

Resolution: unresolved; no resolver link, observed 2026-07-14T10:33:54.851493Z

Source-reported events for the cited work

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Observation eae69f32-f3d9-40a1-b647-63633bc230ea · outbound

This paper cites WebShop: Towards Scalable Real-World Web Interaction with Grounded Language Agents.

Agentic-DPO: From Imitation to Agentic Policy Optimization on Expert Trajectories WebShop: Towards Scalable Real-World Web Interaction with Grounded Language Agents

Reference 45

Resolution: unresolved; no resolver link, observed 2026-07-14T10:33:54.851493Z

Source-reported events for the cited work

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Observation 938b0a07-ceca-4086-8f3c-6be037a98c7f · outbound

This paper cites React: Synergizing reasoning and acting in language models.

Agentic-DPO: From Imitation to Agentic Policy Optimization on Expert Trajectories React: Synergizing reasoning and acting in language models

Reference 46

Resolution: unresolved; no resolver link, observed 2026-07-14T10:33:54.851493Z

Source-reported events for the cited work

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Observation ab2e065b-db38-425d-a90c-00716a677089 · outbound

This paper cites $\tau$-bench: A Benchmark for Tool-Agent-User Interaction in Real-World Domains.

Agentic-DPO: From Imitation to Agentic Policy Optimization on Expert Trajectories $\tau$-bench: A Benchmark for Tool-Agent-User Interaction in Real-World Domains

Reference 47

Resolution: unresolved; no resolver link, observed 2026-07-14T10:33:54.851493Z

Source-reported events for the cited work

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Observation 4763284b-6737-4384-8075-b7db468ec3b4 · outbound

This paper cites Pivotrl: High accuracy agentic post-training at low compute cost.arXiv preprint arXiv:2603.21383, 2026.

Agentic-DPO: From Imitation to Agentic Policy Optimization on Expert Trajectories Pivotrl: High accuracy agentic post-training at low compute cost.arXiv preprint arXiv:2603.21383, 2026

Reference 48

Resolution: unresolved; no resolver link, observed 2026-07-14T10:33:54.851493Z

Source-reported events for the cited work

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Observation e7c1d6fa-76c3-40ae-a3d0-e44b3ee16705 · outbound

This paper cites Self-Rewarding Language Models.

Agentic-DPO: From Imitation to Agentic Policy Optimization on Expert Trajectories Self-Rewarding Language Models

Reference 49

Resolution: unresolved; no resolver link, observed 2026-07-14T10:33:54.851493Z

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Observation 6cb0ce6a-0dd2-45e4-bfe3-044c7ba7c105 · outbound

This paper cites Agenttuning: Enabling generalized agent abilities for llms.

Agentic-DPO: From Imitation to Agentic Policy Optimization on Expert Trajectories Agenttuning: Enabling generalized agent abilities for llms

Reference 50

Resolution: unresolved; no resolver link, observed 2026-07-14T10:33:54.851493Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8fa68c9e-1438-475f-b7bf-7c4778a8b393 · outbound

This paper cites ToolACE-R: Model-aware iterative training and adaptive refinement for tool learning.arXiv preprint arXiv:2504.01400, 2025.

Agentic-DPO: From Imitation to Agentic Policy Optimization on Expert Trajectories ToolACE-R: Model-aware iterative training and adaptive refinement for tool learning.arXiv preprint arXiv:2504.01400, 2025

Reference 51

Resolution: unresolved; no resolver link, observed 2026-07-14T10:33:54.851493Z

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Observation 39c67c93-b0a2-4325-b8c2-92037ed6bb82 · outbound

This paper cites first_name.

Agentic-DPO: From Imitation to Agentic Policy Optimization on Expert Trajectories first_name

Reference 52

Resolution: unresolved; no resolver link, observed 2026-07-14T10:33:54.851493Z

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Observation a3388738-55db-44b5-bc57-f5edc284d166 · outbound

This paper cites user_id":.

Agentic-DPO: From Imitation to Agentic Policy Optimization on Expert Trajectories user_id":

Reference 55

Resolution: unresolved; no resolver link, observed 2026-07-14T10:33:54.851493Z

Source-reported events for the cited work

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Observation ec77e9ca-df12-4c1e-9d2e-954a088d3cab · outbound

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Agentic-DPO: From Imitation to Agentic Policy Optimization on Expert Trajectories name": "Alice

Reference 56

Resolution: unresolved; no resolver link, observed 2026-07-14T10:33:54.851493Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 12432ffd-2b1f-45ec-9999-c5d43c59e399 · outbound

This paper cites user_id":.

Agentic-DPO: From Imitation to Agentic Policy Optimization on Expert Trajectories user_id":

Reference 57

Resolution: unresolved; no resolver link, observed 2026-07-14T10:33:54.851493Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e87c705e-95b9-402d-9331-7386dabd061e · outbound

This paper cites user_id":.

Agentic-DPO: From Imitation to Agentic Policy Optimization on Expert Trajectories user_id":

Reference 58

Resolution: unresolved; no resolver link, observed 2026-07-14T10:33:54.851493Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation dfbbb2dd-43d8-461c-a4a0-dc1205f589a9 · outbound

This paper cites error":.

Agentic-DPO: From Imitation to Agentic Policy Optimization on Expert Trajectories error":

Reference 59

Resolution: unresolved; no resolver link, observed 2026-07-14T10:33:54.851493Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 312805d7-2730-439b-a138-166929038dd1 · outbound

This paper cites user_id":.

Agentic-DPO: From Imitation to Agentic Policy Optimization on Expert Trajectories user_id":

Reference 60

Resolution: unresolved; no resolver link, observed 2026-07-14T10:33:54.851493Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

Observation 875642c6-38ee-4344-9c6f-05ebcba0861e · inbound

Trajectories That Segment Themselves: Agent-Declared Boundaries as a Training Unit cites this paper.

Trajectories That Segment Themselves: Agent-Declared Boundaries as a Training Unit Agentic-DPO: From Imitation to Agentic Policy Optimization on Expert Trajectories

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-04T09:44:31.189084Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c2f3f8ba-9fab-439d-9502-a0dcb14cd4ea · inbound

Trajectories That Segment Themselves: Agent-Declared Boundaries as a Training Unit cites this paper.

Trajectories That Segment Themselves: Agent-Declared Boundaries as a Training Unit Agentic-DPO: From Imitation to Agentic Policy Optimization on Expert Trajectories

Reference 2026

Resolution: metadata mismatch; local_arxiv, observed 2026-08-04T09:49:29.305770Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.