Learning Agentic Policy from Action Guidance | Paper Citation Record

Observation 1639d43e-7ea4-4e77-9b48-1f80e58be624 · outbound

This paper cites Claude Opus 4.6 model card.

Learning Agentic Policy from Action Guidance Claude Opus 4.6 model card

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-05-13T11:07:39.831018Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 5691e81c-db21-469e-914f-a15cb41526d4 · outbound

This paper cites $\tau^2$-Bench: Evaluating Conversational Agents in a Dual-Control Environment.

Learning Agentic Policy from Action Guidance $\tau^2$-Bench: Evaluating Conversational Agents in a Dual-Control Environment

Reference 2

Resolution: verified exact; local_arxiv, observed 2026-05-13T05:07:17.802510Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation cf10f969-fe6e-4803-be0b-9cd962e0eda4 · outbound

This paper cites Fine- tuning web agents: It works, but it’s trickier than you think.

Learning Agentic Policy from Action Guidance Fine- tuning web agents: It works, but it’s trickier than you think

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-05-13T11:07:39.816125Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation f2176436-10eb-4ea0-bbf8-da2b115a9287 · outbound

This paper cites SFT or RL? An Early Investigation into Training R1-Like Reasoning Large Vision-Language Models.

Learning Agentic Policy from Action Guidance SFT or RL? An Early Investigation into Training R1-Like Reasoning Large Vision-Language Models

Reference 4

Resolution: verified exact; arxiv_id, observed 2026-05-17T15:43:34.765363Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation a2d792c1-5c8e-444f-8ae2-27ecb873b73a · outbound

This paper cites xbench: Tracking Agents Productivity Scaling with Profession-Aligned Real-World Evaluations.

Learning Agentic Policy from Action Guidance xbench: Tracking Agents Productivity Scaling with Profession-Aligned Real-World Evaluations

Reference 5

Resolution: verified exact; arxiv_id, observed 2026-05-13T05:07:17.799791Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 357d6141-dc4b-4d87-8a14-a127566deef6 · outbound

This paper cites Beyond Two-Stage Training: Cooperative SFT and RL for LLM Reasoning.

Learning Agentic Policy from Action Guidance Beyond Two-Stage Training: Cooperative SFT and RL for LLM Reasoning

Reference 6

Resolution: verified exact; arxiv_id, observed 2026-06-02T02:03:33.109892Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 987e40b8-e036-4fc2-85eb-758838d08a28 · outbound

This paper cites GPG: A simple and strong reinforcement learning baseline for model reasoning.

Learning Agentic Policy from Action Guidance GPG: A simple and strong reinforcement learning baseline for model reasoning

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-05-13T11:07:39.825455Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation fbe351f8-dc2b-4f91-8b4d-b0e3a4584bae · outbound

This paper cites Redsearcher: A scalable and cost-efficient framework for long-horizon search agents.

Learning Agentic Policy from Action Guidance Redsearcher: A scalable and cost-efficient framework for long-horizon search agents

Reference 8

Resolution: verified exact; arxiv_id, observed 2026-05-13T05:07:17.587389Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation b3e77c83-4a51-4118-9c6d-fc14d1fed60d · outbound

This paper cites Harder is better: Boosting mathematical reasoning via difficulty-aware GRPO and multi-aspect question reformulation.

Learning Agentic Policy from Action Guidance Harder is better: Boosting mathematical reasoning via difficulty-aware GRPO and multi-aspect question reformulation

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-05-13T11:07:39.776771Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation de9932e6-8f5c-4a75-86a9-c4c8d28d60d8 · outbound

This paper cites Mind2web: Towards a generalist agent for the web.Advances in Neural Information Processing Systems, 36:28091–28114.

Learning Agentic Policy from Action Guidance Mind2web: Towards a generalist agent for the web.Advances in Neural Information Processing Systems, 36:28091–28114

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-05-13T11:07:39.818141Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation f5550734-41ba-41e7-80b5-8341adc77647 · outbound

This paper cites OpenVLThinker: Complex Vision-Language Reasoning via Iterative SFT-RL Cycles.

Learning Agentic Policy from Action Guidance OpenVLThinker: Complex Vision-Language Reasoning via Iterative SFT-RL Cycles

Reference 11

Resolution: verified exact; arxiv_id, observed 2026-05-19T06:59:03.519833Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation a0d8fdd6-9f20-4899-a0f7-7fd7a4f750e6 · outbound

This paper cites Wildclawbench.

Learning Agentic Policy from Action Guidance Wildclawbench

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-05-13T11:07:39.823623Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 751d4fcb-e620-4ec5-abcc-dc6947058c96 · outbound

This paper cites Tool-Star: Empowering LLM-Brained Multi-Tool Reasoner via Reinforcement Learning.

Learning Agentic Policy from Action Guidance Tool-Star: Empowering LLM-Brained Multi-Tool Reasoner via Reinforcement Learning

Reference 13

Resolution: verified exact; arxiv_id, observed 2026-05-13T05:07:17.727198Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 37eda508-fa87-42b7-be1d-f85b7e9c116e · outbound

This paper cites Agentic Reinforced Policy Optimization.

Learning Agentic Policy from Action Guidance Agentic Reinforced Policy Optimization

Reference 14

Resolution: verified exact; arxiv_id, observed 2026-05-17T02:57:12.173630Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation bec250b6-7d34-4c30-bf01-d732a8d11b3a · outbound

This paper cites Agent-World: Scaling Real-World Environment Synthesis for Evolving General Agent Intelligence.

Learning Agentic Policy from Action Guidance Agent-World: Scaling Real-World Environment Synthesis for Evolving General Agent Intelligence

Reference 15

Resolution: verified exact; local_arxiv, observed 2026-05-13T05:07:17.621909Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 5efedcd7-945a-4fa4-8eda-6e88d8374c97 · outbound

This paper cites Plan-and-Act: Improving Planning of Agents for Long-Horizon Tasks.

Learning Agentic Policy from Action Guidance Plan-and-Act: Improving Planning of Agents for Long-Horizon Tasks

Reference 16

Resolution: verified exact; arxiv_id, observed 2026-05-17T21:32:18.834213Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 4a61f7ee-90f2-460f-9b23-03f66a981999 · outbound

This paper cites Group-in-Group Policy Optimization for LLM Agent Training.

Learning Agentic Policy from Action Guidance Group-in-Group Policy Optimization for LLM Agent Training

Reference 17

Resolution: verified exact; local_arxiv, observed 2026-05-13T05:07:17.632913Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 859ffaa4-7137-4c9d-bc2d-2b543d12052e · outbound

This paper cites SRFT: A Single-Stage Method with Supervised and Reinforcement Fine-Tuning for Reasoning.

Learning Agentic Policy from Action Guidance SRFT: A Single-Stage Method with Supervised and Reinforcement Fine-Tuning for Reasoning

Reference 18

Resolution: verified exact; arxiv_id, observed 2026-05-13T05:07:17.645623Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 1e817b8d-464e-40b4-b648-316d0bf06b9a · outbound

This paper cites Beyond ten turns: Unlocking long-horizon agentic search with large-scale asynchronous rl.

Learning Agentic Policy from Action Guidance Beyond ten turns: Unlocking long-horizon agentic search with large-scale asynchronous rl

Reference 19

Resolution: verified exact; arxiv_id, observed 2026-05-13T05:07:17.649435Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 009f5b83-d135-430d-a75b-484f7a33c050 · outbound

This paper cites Actor-curator: Co-adaptive curriculum learning via policy-improvement bandits for rl post-training.arXiv preprint arXiv:2602.20532.

Learning Agentic Policy from Action Guidance Actor-curator: Co-adaptive curriculum learning via policy-improvement bandits for rl post-training.arXiv preprint arXiv:2602.20532

Reference 20

Resolution: verified exact; arxiv_id, observed 2026-05-13T05:07:17.590870Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 3af81f34-30cf-47d1-bbd8-d1e391518386 · outbound

This paper cites Deep q-learning from demonstrations.

Learning Agentic Policy from Action Guidance Deep q-learning from demonstrations

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-05-13T11:07:39.778716Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 50fbd06d-8e5a-424c-bb98-d95aa186f427 · outbound

This paper cites Boosting mllm reasoning with text-debiased hint-grpo.

Learning Agentic Policy from Action Guidance Boosting mllm reasoning with text-debiased hint-grpo

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-05-13T11:07:39.798467Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation b82336af-dcd4-47ec-bc91-ffc20befc04b · outbound

This paper cites Reinforcement Learning via Self-Distillation.

Learning Agentic Policy from Action Guidance Reinforcement Learning via Self-Distillation

Reference 23

Resolution: verified exact; local_arxiv, observed 2026-05-13T05:07:17.597502Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 08144029-b394-4e13-8a6d-176e06855d6c · outbound

This paper cites Tree search for LLM agent reinforcement learning.

Learning Agentic Policy from Action Guidance Tree search for LLM agent reinforcement learning

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-05-13T11:07:39.780576Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 0be9105e-bea4-4158-beac-d4f44700b3c5 · outbound

This paper cites Thinking with map: Reinforced parallel map-augmented agent for geolocalization.ACL.

Learning Agentic Policy from Action Guidance Thinking with map: Reinforced parallel map-augmented agent for geolocalization.ACL

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-05-13T11:07:39.812409Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 8635f681-43ae-46dd-94c9-dfaef4cb5faa · outbound

This paper cites Vcrl: Variance-based curriculum reinforcement learning for large language models.

Learning Agentic Policy from Action Guidance Vcrl: Variance-based curriculum reinforcement learning for large language models

Reference 26

Resolution: verified exact; arxiv_id, observed 2026-05-13T05:07:17.676707Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 80061224-480d-4b83-b19b-6846e08e04c9 · outbound

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

Learning Agentic Policy from Action Guidance SWE-bench: Can Language Models Resolve Real-World GitHub Issues?

Reference 27

Resolution: verified exact; local_arxiv, observed 2026-05-13T05:07:17.688917Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation afda1e21-8088-47ae-9ae2-b7a6effdc9d4 · outbound

This paper cites Search-R1: Training LLMs to Reason and Leverage Search Engines with Reinforcement Learning.

Learning Agentic Policy from Action Guidance Search-R1: Training LLMs to Reason and Leverage Search Engines with Reinforcement Learning

Reference 28

Resolution: verified exact; local_arxiv, observed 2026-05-13T05:07:17.606250Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 52a638ee-6fb7-4688-97ab-09df64d5fd0f · outbound

This paper cites WebSailor: Navigating Super-human Reasoning for Web Agent.

Learning Agentic Policy from Action Guidance WebSailor: Navigating Super-human Reasoning for Web Agent

Reference 29

Resolution: verified exact; arxiv_id, observed 2026-05-17T15:37:09.773663Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation b970073b-9c08-4745-9f20-11aae7c9e7b9 · outbound

This paper cites Adacurl: Adaptive curriculum reinforcement learning with invalid sample mitigation and historical revisiting.

Learning Agentic Policy from Action Guidance Adacurl: Adaptive curriculum reinforcement learning with invalid sample mitigation and historical revisiting

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-05-13T11:07:39.814168Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 55358957-318a-4682-941b-47aa643d0dac · outbound

This paper cites WebThinker: Empowering Large Reasoning Models with Deep Research Capability.

Learning Agentic Policy from Action Guidance WebThinker: Empowering Large Reasoning Models with Deep Research Capability

Reference 31

Resolution: verified exact; arxiv_id, observed 2026-05-16T19:14:25.573630Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 92c5b202-9bfe-4c5c-b64a-08617c072a60 · outbound

This paper cites Squeeze the Soaked Sponge: Efficient Off-policy Reinforcement Finetuning for Large Language Model.

Learning Agentic Policy from Action Guidance Squeeze the Soaked Sponge: Efficient Off-policy Reinforcement Finetuning for Large Language Model

Reference 32

Resolution: verified exact; arxiv_id, observed 2026-05-13T05:07:17.618672Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation cfd20f2a-199e-4d14-ae46-5d9da119b7bb · outbound

This paper cites Guided exploration with proximal policy optimization using a single demonstration.

Learning Agentic Policy from Action Guidance Guided exploration with proximal policy optimization using a single demonstration

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-05-13T11:07:39.821791Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 66b3e568-7ed0-4777-bb46-04d01342c9e7 · outbound

This paper cites Truthfulqa: Measuring how models mimic hu- man falsehoods.

Learning Agentic Policy from Action Guidance Truthfulqa: Measuring how models mimic hu- man falsehoods

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-05-13T11:07:39.820085Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 8caa542f-b769-4914-8565-be8a51ce2d1a · outbound

This paper cites DeepSeek-V3 Technical Report.

Learning Agentic Policy from Action Guidance DeepSeek-V3 Technical Report

Reference 35

Resolution: verified exact; local_arxiv, observed 2026-05-13T05:07:17.796733Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 2361523b-ecd7-42c7-a7aa-dac6d962aa89 · outbound

This paper cites Large Language Model Agent: A Survey on Methodology, Applications and Challenges.

Learning Agentic Policy from Action Guidance Large Language Model Agent: A Survey on Methodology, Applications and Challenges

Reference 36

Resolution: verified exact; local_arxiv, observed 2026-05-13T05:07:17.594178Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation b737ab17-d520-460c-a169-a0d582078b9c · outbound

This paper cites Learning what reinforcement learning can't: Interleaved online fine-tuning for hardest questions.

Learning Agentic Policy from Action Guidance Learning what reinforcement learning can't: Interleaved online fine-tuning for hardest questions

Reference 37

Resolution: verified exact; arxiv_id, observed 2026-05-13T05:07:17.787952Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 826ac4a2-1c2d-4ade-9115-ee3388b75676 · outbound

This paper cites SkillClaw: Let Skills Evolve Collectively with Agentic Evolver.

Learning Agentic Policy from Action Guidance SkillClaw: Let Skills Evolve Collectively with Agentic Evolver

Reference 38

Resolution: verified exact; local_arxiv, observed 2026-05-13T05:07:17.776747Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation cf36beac-9f5d-45f3-9e96-e84907002074 · outbound

This paper cites GAIA: a benchmark for General AI Assistants.

Learning Agentic Policy from Action Guidance GAIA: a benchmark for General AI Assistants

Reference 39

Resolution: verified exact; local_arxiv, observed 2026-05-13T05:07:17.603371Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 14e1b130-4e90-4896-aea3-d3393945bed6 · outbound

This paper cites Minimax m2.1 system card.

Learning Agentic Policy from Action Guidance Minimax m2.1 system card

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-05-13T11:07:39.829342Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation bdfa9d7a-d43d-4f0b-b3dc-39f5de834860 · outbound

This paper cites Over- coming exploration in reinforcement learning with demonstrations.

Learning Agentic Policy from Action Guidance Over- coming exploration in reinforcement learning with demonstrations

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-05-13T11:07:39.806867Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 2cf22fff-26b7-4055-817f-97f6907687bc · outbound

This paper cites Adaptive Guidance Accelerates Reinforcement Learning of Reasoning Models.

Learning Agentic Policy from Action Guidance Adaptive Guidance Accelerates Reinforcement Learning of Reasoning Models

Reference 42

Resolution: metadata mismatch; arxiv_id, observed 2026-05-13T05:07:17.751601Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation a00052b6-468d-4553-a72a-21ff3ba94931 · outbound

This paper cites Gpt-5.4 thinking system card.

Learning Agentic Policy from Action Guidance Gpt-5.4 thinking system card

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-05-13T11:07:39.810613Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation b097e1fa-7b21-481a-acf6-bde3b98e240a · outbound

This paper cites Iterative reasoning preference optimization.Advances in Neural Information Processing Systems, 37:116617–116637.

Learning Agentic Policy from Action Guidance Iterative reasoning preference optimization.Advances in Neural Information Processing Systems, 37:116617–116637

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-05-13T11:07:39.802870Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 2fba3040-3165-4db1-8b1a-54dc1ca17d4e · outbound

This paper cites UI-TARS: Pioneering Automated GUI Interaction with Native Agents.

Learning Agentic Policy from Action Guidance UI-TARS: Pioneering Automated GUI Interaction with Native Agents

Reference 45

Resolution: verified exact; local_arxiv, observed 2026-05-13T05:07:17.699644Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation a57effd7-b998-4217-8826-62f5937f4fe7 · outbound

This paper cites Learning Complex Dexterous Manipulation with Deep Reinforcement Learning and Demonstrations.

Learning Agentic Policy from Action Guidance Learning Complex Dexterous Manipulation with Deep Reinforcement Learning and Demonstrations

Reference 46

Resolution: verified exact; arxiv_id, observed 2026-05-13T05:07:17.755910Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 9c44e73f-ff6c-4474-9312-9bd559a419c2 · outbound

This paper cites GPQA: A Graduate-Level Google-Proof Q&A Benchmark.

Learning Agentic Policy from Action Guidance GPQA: A Graduate-Level Google-Proof Q&A Benchmark

Reference 47

Resolution: verified exact; local_arxiv, observed 2026-05-13T05:07:17.773989Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation d467159e-4082-404f-8d31-199b819351da · outbound

This paper cites Proximal Policy Optimization Algorithms.

Learning Agentic Policy from Action Guidance Proximal Policy Optimization Algorithms

Reference 48

Resolution: verified exact; local_arxiv, observed 2026-05-13T05:07:17.600157Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 1633b736-430c-4d9c-862d-6bc615c3575a · outbound

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

Learning Agentic Policy from Action Guidance DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models

Reference 49

Resolution: verified exact; local_arxiv, observed 2026-05-13T05:07:17.696982Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation fc9c0f91-b266-4d95-b705-4562cdd3e576 · outbound

This paper cites Self-Distillation Enables Continual Learning.

Learning Agentic Policy from Action Guidance Self-Distillation Enables Continual Learning

Reference 50

Resolution: verified exact; local_arxiv, observed 2026-05-13T05:07:17.685825Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation fa15cef6-ae82-438f-9a32-97dd9aa78c15 · outbound

This paper cites OpenAI GPT-5 System Card.

Learning Agentic Policy from Action Guidance OpenAI GPT-5 System Card

Reference 51

Resolution: verified exact; local_arxiv, observed 2026-05-13T05:07:17.679643Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 95c47958-1b89-4fc5-ad6c-b886c217c52a · outbound

This paper cites Kimi K2.5: Visual Agentic Intelligence.

Learning Agentic Policy from Action Guidance Kimi K2.5: Visual Agentic Intelligence

Reference 52

Resolution: verified exact; local_arxiv, observed 2026-05-13T05:07:17.768488Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 485864f6-187f-404a-a5ab-3f6c1c9529ab · outbound

This paper cites Qwen3.5: Accelerating productivity with native multimodal agents, February.

Learning Agentic Policy from Action Guidance Qwen3.5: Accelerating productivity with native multimodal agents, February

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-05-13T11:07:39.805037Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation e2912aa8-4b67-4e68-b97c-f70d1e74a105 · outbound

This paper cites an unresolved cited work.

Learning Agentic Policy from Action Guidance Unresolved cited work

Reference 54

Resolution: unresolved; raw_fallback, observed 2026-05-13T11:07:39.800591Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 68746824-b279-4f01-862c-ff4041c7e0d0 · outbound

This paper cites Tongyi DeepResearch Technical Report.

Learning Agentic Policy from Action Guidance Tongyi DeepResearch Technical Report

Reference 56

Resolution: verified exact; arxiv_id, observed 2026-05-15T08:56:57.433092Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation f4e2c5f8-93f7-4bc0-b219-a26eaf80f776 · outbound

This paper cites Language models don’t always say what they think: Unfaithful explanations in chain-of-thought prompting.Advances in Neural Information Processing Systems, 36:74952–74965.

Learning Agentic Policy from Action Guidance Language models don’t always say what they think: Unfaithful explanations in chain-of-thought prompting.Advances in Neural Information Processing Systems, 36:74952–74965

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-05-13T11:07:39.774687Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 8dc39b33-8e3c-4204-ba86-c86d6618adae · outbound

This paper cites Deep Reinforcement Learning and the Deadly Triad.

Learning Agentic Policy from Action Guidance Deep Reinforcement Learning and the Deadly Triad

Reference 58

Resolution: verified exact; arxiv_id, observed 2026-05-13T05:07:17.711777Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 87baeeb7-af4a-4cee-b02e-f8467ffb4008 · outbound

This paper cites Leveraging Demonstrations for Deep Reinforcement Learning on Robotics Problems with Sparse Rewards.

Learning Agentic Policy from Action Guidance Leveraging Demonstrations for Deep Reinforcement Learning on Robotics Problems with Sparse Rewards

Reference 59

Resolution: verified exact; arxiv_id, observed 2026-05-13T05:07:17.758867Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 74e5c648-4a6d-4977-9805-bacabdda8f9f · outbound

This paper cites Let it flow: Agentic crafting on rock and roll, building the rome model within an open agentic learning ecosystem.

Learning Agentic Policy from Action Guidance Let it flow: Agentic crafting on rock and roll, building the rome model within an open agentic learning ecosystem

Reference 60

Resolution: verified exact; arxiv_id, observed 2026-05-13T05:07:17.761647Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation db34b9fc-5d71-432d-993e-62773e8d8907 · outbound

This paper cites OpenClaw-RL: Train Any Agent Simply by Talking.

Learning Agentic Policy from Action Guidance OpenClaw-RL: Train Any Agent Simply by Talking

Reference 61

Resolution: verified exact; local_arxiv, observed 2026-05-13T05:07:17.765019Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 7711b20f-5074-4cc0-b9e5-82086d47af10 · outbound

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

Learning Agentic Policy from Action Guidance RAGEN: Understanding Self-Evolution in LLM Agents via Multi-Turn Reinforcement Learning

Reference 62

Resolution: verified exact; arxiv_id, observed 2026-05-13T07:13:34.687091Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation f9d25609-d38b-42b2-8332-d678f8004c54 · outbound

This paper cites Agentic Reasoning for Large Language Models.

Learning Agentic Policy from Action Guidance Agentic Reasoning for Large Language Models

Reference 63

Resolution: verified exact; arxiv_id, observed 2026-05-17T15:14:26.657956Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation f4c1fd2f-5f2e-44e6-af5b-fb0dce07b088 · outbound

This paper cites WebWalker: Benchmarking LLMs in Web Traversal.

Learning Agentic Policy from Action Guidance WebWalker: Benchmarking LLMs in Web Traversal

Reference 64

Resolution: metadata mismatch; arxiv_id, observed 2026-05-13T05:07:17.791143Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 82846ed5-0a12-479c-a3b3-b13b3b5eddd7 · outbound

This paper cites Learn hard problems during rl with reference guided fine-tuning.

Learning Agentic Policy from Action Guidance Learn hard problems during rl with reference guided fine-tuning

Reference 65

Resolution: verified exact; arxiv_id, observed 2026-05-13T05:07:17.702708Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation a51275f2-6cbc-4d04-9b51-8f2c2dfe705d · outbound

This paper cites Osworld: Benchmarking multimodal agents for open-ended tasks in real computer environments.

Learning Agentic Policy from Action Guidance Osworld: Benchmarking multimodal agents for open-ended tasks in real computer environments

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-05-13T11:07:39.808900Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 1d69ddf5-36fb-4f9f-ae7f-c240932e4ed3 · outbound

This paper cites Learning to Reason under Off-Policy Guidance.

Learning Agentic Policy from Action Guidance Learning to Reason under Off-Policy Guidance

Reference 68

Resolution: verified exact; arxiv_id, observed 2026-05-15T23:17:03.075876Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 64b3bdc3-82c7-4ed2-8889-7e699ce8bea5 · outbound

This paper cites GUI-Libra: Training Native GUI Agents to Reason and Act with Action-aware Supervision and Partially Verifiable RL.

Learning Agentic Policy from Action Guidance GUI-Libra: Training Native GUI Agents to Reason and Act with Action-aware Supervision and Partially Verifiable RL

Reference 69

Resolution: verified exact; arxiv_id, observed 2026-05-27T02:04:34.683381Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 9f6a887b-9d47-4c88-ae0f-10733a8b4686 · outbound

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

Learning Agentic Policy from Action Guidance React: Synergizing reasoning and acting in language models

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-05-13T11:07:39.827410Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation afad9ae9-cc3b-4580-a25f-ea603716bab3 · outbound

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

Learning Agentic Policy from Action Guidance $\tau$-bench: A Benchmark for Tool-Agent-User Interaction in Real-World Domains

Reference 71

Resolution: verified exact; local_arxiv, observed 2026-05-13T05:07:17.666389Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 69d3835c-60f0-442b-9165-e06fad3beb15 · outbound

This paper cites Coba-rl: Capability-oriented budget allocation for reinforcement learning in llms.

Learning Agentic Policy from Action Guidance Coba-rl: Capability-oriented budget allocation for reinforcement learning in llms

Reference 72

Resolution: verified exact; arxiv_id, observed 2026-05-13T05:07:17.669790Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 7f6c0cb2-ad96-4afd-8d81-644cb60a574a · outbound

This paper cites arXiv preprint arXiv:2603.21383 , year=.

Learning Agentic Policy from Action Guidance arXiv preprint arXiv:2603.21383 , year=

Reference 73

Resolution: verified exact; arxiv_id, observed 2026-05-13T05:07:17.663444Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 41b76d43-58f0-4bec-a003-97980b44661c · outbound

This paper cites MedResearcher-R1: Expert-Level Medical Deep Researcher via A Knowledge-Informed Trajectory Synthesis Framework.

Learning Agentic Policy from Action Guidance MedResearcher-R1: Expert-Level Medical Deep Researcher via A Knowledge-Informed Trajectory Synthesis Framework

Reference 74

Resolution: verified exact; arxiv_id, observed 2026-05-13T05:07:17.656657Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 1c777d36-c31c-4b72-bea6-5de65d9332bc · outbound

This paper cites DAPO: An Open-Source LLM Reinforcement Learning System at Scale.

Learning Agentic Policy from Action Guidance DAPO: An Open-Source LLM Reinforcement Learning System at Scale

Reference 75

Resolution: verified exact; local_arxiv, observed 2026-05-13T05:07:17.694521Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 66a6d182-aa4a-4d38-b0c8-a96694d6572c · outbound

This paper cites Does Reinforcement Learning Really Incentivize Reasoning Capacity in LLMs Beyond the Base Model?.

Learning Agentic Policy from Action Guidance Does Reinforcement Learning Really Incentivize Reasoning Capacity in LLMs Beyond the Base Model?

Reference 76

Resolution: verified exact; local_arxiv, observed 2026-05-13T05:07:17.659881Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation a850c3b3-a2dc-4ad3-8e8c-acf481ef8173 · outbound

This paper cites Agentevolver: Towards efficient self-evolving agent system.

Learning Agentic Policy from Action Guidance Agentevolver: Towards efficient self-evolving agent system

Reference 77

Resolution: verified exact; arxiv_id, observed 2026-05-13T05:07:17.785140Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation ff787be5-ad14-4556-bcbd-6f07b151209e · outbound

This paper cites The Landscape of Agentic Reinforcement Learning for LLMs: A Survey.

Learning Agentic Policy from Action Guidance The Landscape of Agentic Reinforcement Learning for LLMs: A Survey

Reference 78

Resolution: verified exact; local_arxiv, observed 2026-05-13T05:07:17.608879Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 5eddc1e9-79b6-4ecb-998d-34d290444f99 · outbound

This paper cites arXiv preprint arXiv:2508.11408 , year=.

Learning Agentic Policy from Action Guidance arXiv preprint arXiv:2508.11408 , year=

Reference 79

Resolution: metadata mismatch; arxiv_id, observed 2026-05-13T05:07:17.705737Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation d45a8540-4c29-4f0d-8030-aef3cc060ce8 · outbound

This paper cites Self-Distilled Reasoner: On-Policy Self-Distillation for Large Language Models.

Learning Agentic Policy from Action Guidance Self-Distilled Reasoner: On-Policy Self-Distillation for Large Language Models

Reference 80

Resolution: verified exact; local_arxiv, observed 2026-05-13T05:07:17.779421Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 4bd1baa4-5550-45eb-b3a4-96e166e18099 · outbound

This paper cites Prosperity before collapse: How far can off-policy rl reach with stale data on llms?.

Learning Agentic Policy from Action Guidance Prosperity before collapse: How far can off-policy rl reach with stale data on llms?

Reference 81

Resolution: verified exact; arxiv_id, observed 2026-05-13T05:07:17.708884Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 379ba4ab-3a65-469a-bd7e-d9cdcdfc50cf · outbound

This paper cites Code2world: A gui world model via renderable code generation.

Learning Agentic Policy from Action Guidance Code2world: A gui world model via renderable code generation

Reference 82

Resolution: verified exact; arxiv_id, observed 2026-05-13T05:07:17.639387Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 66043e16-8a57-43b6-9729-a26b0da6908c · outbound

This paper cites Instruction-Following Evaluation for Large Language Models.

Learning Agentic Policy from Action Guidance Instruction-Following Evaluation for Large Language Models

Reference 83

Resolution: verified exact; local_arxiv, observed 2026-05-13T05:07:17.642066Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 3bb80b65-9f3d-4e17-a552-07be46bb0b8d · outbound

This paper cites BrowseComp-ZH: Benchmarking Web Browsing Ability of Large Language Models in Chinese.

Learning Agentic Policy from Action Guidance BrowseComp-ZH: Benchmarking Web Browsing Ability of Large Language Models in Chinese

Reference 84

Resolution: metadata mismatch; arxiv_id, observed 2026-05-17T22:04:50.009871Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Coverage vector

Reference resolution

Outbound references

Pith citing papers