Paper Citation Record · LEDGER

Information Gain-based Rollout Policy Optimization: An Adaptive Tree-Structured Rollout Approach for Multi-Turn LLM Agents

As of 17 August 2026, this Paper Citation Record lists 37 of 37 outbound references and 2 inbound Pith citation observations for arXiv:2607.06223.

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

pith.paper-citation-record.v1
2607.06223 v1

Coverage vector

measured 37 of 37 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-07-08T13:01:45.049174Z

measured 39 of 39 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-16T06:30:59.297886+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T05:53:52.763644Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-06T05:53:52.854862Z

Reference resolution

37 of 37 outbound references displayed

verified exact20
verified fuzzy16
unresolved1
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 653bad21-17df-4070-bd47-d3474750dee1 · outbound

This paper cites Language models are few-shot learners.Advances in neural information processing systems, 33:1877–1901, 2020.

Information Gain-based Rollout Policy Optimization: An Adaptive Tree-Structured Rollout Approach for Multi-Turn LLM Agents Language models are few-shot learners.Advances in neural information processing systems, 33:1877–1901, 2020

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-07-08T13:04:57.061259Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 32b1664c-bd0d-4432-91aa-a452b6374cef · outbound

This paper cites Palm: Scaling language modeling with pathways.Journal of machine learning research, 24(240): 1–113, 2023.

Information Gain-based Rollout Policy Optimization: An Adaptive Tree-Structured Rollout Approach for Multi-Turn LLM Agents Palm: Scaling language modeling with pathways.Journal of machine learning research, 24(240): 1–113, 2023

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-07-08T13:04:57.083108Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation f1d3c3ec-21cd-4711-8191-2f83006a4011 · outbound

This paper cites LLaMA: Open and Efficient Foundation Language Models.

Information Gain-based Rollout Policy Optimization: An Adaptive Tree-Structured Rollout Approach for Multi-Turn LLM Agents LLaMA: Open and Efficient Foundation Language Models

Reference 3

Resolution: verified exact; local_arxiv, observed 2026-07-08T13:04:56.738338Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation eecfc933-8c46-4f11-83ac-80370982ca02 · outbound

This paper cites ReAct: Synergizing Reasoning and Acting in Language Models.

Information Gain-based Rollout Policy Optimization: An Adaptive Tree-Structured Rollout Approach for Multi-Turn LLM Agents ReAct: Synergizing Reasoning and Acting in Language Models

Reference 4

Resolution: verified exact; local_arxiv, observed 2026-07-08T13:04:56.701696Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 6eb9376e-9dd2-4705-9c20-335efced767d · outbound

This paper cites Toolformer: Language models can teach themselves to use tools.Advances in neural information processing systems, 36: 68539–68551.

Information Gain-based Rollout Policy Optimization: An Adaptive Tree-Structured Rollout Approach for Multi-Turn LLM Agents Toolformer: Language models can teach themselves to use tools.Advances in neural information processing systems, 36: 68539–68551

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-07-08T13:04:57.094150Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 59fbeac2-527b-467f-85be-ae71a3a1a923 · outbound

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

Information Gain-based Rollout Policy Optimization: An Adaptive Tree-Structured Rollout Approach for Multi-Turn LLM Agents The Landscape of Agentic Reinforcement Learning for LLMs: A Survey

Reference 6

Resolution: verified exact; local_arxiv, observed 2026-07-08T13:04:56.742129Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 1a918ce6-6f2d-4f62-853f-055961e0244e · outbound

This paper cites Deep Research Agents: A Systematic Examination And Roadmap.

Information Gain-based Rollout Policy Optimization: An Adaptive Tree-Structured Rollout Approach for Multi-Turn LLM Agents Deep Research Agents: A Systematic Examination And Roadmap

Reference 7

Resolution: verified exact; local_arxiv, observed 2026-07-08T13:04:56.716778Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 3555b2ba-7936-4a1e-b79c-7e9d3df39f71 · outbound

This paper cites Searching for best practices in retrieval- augmented generation.

Information Gain-based Rollout Policy Optimization: An Adaptive Tree-Structured Rollout Approach for Multi-Turn LLM Agents Searching for best practices in retrieval- augmented generation

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-07-08T13:04:57.091978Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation b38c324e-277e-4cc6-846a-2aab5023bafa · outbound

This paper cites Search-o1: Agentic search-enhanced large reasoning models.

Information Gain-based Rollout Policy Optimization: An Adaptive Tree-Structured Rollout Approach for Multi-Turn LLM Agents Search-o1: Agentic search-enhanced large reasoning models

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-07-08T13:04:57.096554Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 6d71ee68-0fae-4634-be81-d148ace315d6 · outbound

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

Information Gain-based Rollout Policy Optimization: An Adaptive Tree-Structured Rollout Approach for Multi-Turn LLM Agents Search-R1: Training LLMs to Reason and Leverage Search Engines with Reinforcement Learning

Reference 10

Resolution: verified exact; local_arxiv, observed 2026-07-08T13:04:56.721618Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation cc264f8a-82bf-408a-8192-331fc975c8ec · outbound

This paper cites ToolRL: Reward is All Tool Learning Needs.

Information Gain-based Rollout Policy Optimization: An Adaptive Tree-Structured Rollout Approach for Multi-Turn LLM Agents ToolRL: Reward is All Tool Learning Needs

Reference 11

Resolution: verified exact; local_arxiv, observed 2026-07-08T13:04:56.719157Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 8f58128f-f53e-43de-9396-3f476fc3f513 · outbound

This paper cites Proximal Policy Optimization Algorithms.

Information Gain-based Rollout Policy Optimization: An Adaptive Tree-Structured Rollout Approach for Multi-Turn LLM Agents Proximal Policy Optimization Algorithms

Reference 12

Resolution: verified exact; local_arxiv, observed 2026-07-08T13:04:56.711606Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation ea69393f-50df-47b8-9222-cda18a03251d · outbound

This paper cites Training language models to follow instructions with human feedback.Advances in neural information processing systems, 35:27730–27744.

Information Gain-based Rollout Policy Optimization: An Adaptive Tree-Structured Rollout Approach for Multi-Turn LLM Agents Training language models to follow instructions with human feedback.Advances in neural information processing systems, 35:27730–27744

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-07-08T13:04:57.085272Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 5db26127-3237-46a2-8a55-3a47aa4ea8f9 · outbound

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

Information Gain-based Rollout Policy Optimization: An Adaptive Tree-Structured Rollout Approach for Multi-Turn LLM Agents DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models

Reference 14

Resolution: verified exact; local_arxiv, observed 2026-07-08T13:04:56.723999Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 924d65f0-371c-4ed0-9a18-57c48f7c031f · outbound

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

Information Gain-based Rollout Policy Optimization: An Adaptive Tree-Structured Rollout Approach for Multi-Turn LLM Agents Group-in-Group Policy Optimization for LLM Agent Training

Reference 15

Resolution: verified exact; local_arxiv, observed 2026-07-08T13:04:56.714142Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 5517b29e-903c-4223-89a1-f84079b4f418 · outbound

This paper cites Information gain-based policy optimization: A simple and effective approach for multi-turn llm agents.

Information Gain-based Rollout Policy Optimization: An Adaptive Tree-Structured Rollout Approach for Multi-Turn LLM Agents Information gain-based policy optimization: A simple and effective approach for multi-turn llm agents

Reference 16

Resolution: verified exact; arxiv_id, observed 2026-07-08T13:04:56.696136Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 957ff1d1-e873-46b1-b8d2-06258fc57d2f · outbound

This paper cites Tree search for llm agent reinforcement learning.

Information Gain-based Rollout Policy Optimization: An Adaptive Tree-Structured Rollout Approach for Multi-Turn LLM Agents Tree search for llm agent reinforcement learning

Reference 17

Resolution: verified exact; arxiv_id, observed 2026-07-08T13:04:56.744901Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation f41b96ba-06e1-48cb-af03-3eb52abc5429 · outbound

This paper cites Agentic entropy-balanced policy optimization.

Information Gain-based Rollout Policy Optimization: An Adaptive Tree-Structured Rollout Approach for Multi-Turn LLM Agents Agentic entropy-balanced policy optimization

Reference 18

Resolution: verified exact; arxiv_id, observed 2026-07-08T13:04:56.704280Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 746a0598-b11f-487d-9302-53595c7fd4f5 · outbound

This paper cites DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning.

Information Gain-based Rollout Policy Optimization: An Adaptive Tree-Structured Rollout Approach for Multi-Turn LLM Agents DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Reference 19

Resolution: verified exact; local_arxiv, observed 2026-07-08T13:04:56.709206Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 533705bc-d6b6-4971-afa2-0eb313595668 · outbound

This paper cites Back to basics: Revisiting reinforce-style optimization for learning from human feedback in llms.

Information Gain-based Rollout Policy Optimization: An Adaptive Tree-Structured Rollout Approach for Multi-Turn LLM Agents Back to basics: Revisiting reinforce-style optimization for learning from human feedback in llms

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-07-08T13:04:57.087498Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 1ace522c-3a58-4e76-a26b-5012f4e794d6 · outbound

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

Information Gain-based Rollout Policy Optimization: An Adaptive Tree-Structured Rollout Approach for Multi-Turn LLM Agents DAPO: An Open-Source LLM Reinforcement Learning System at Scale

Reference 21

Resolution: verified exact; local_arxiv, observed 2026-07-08T13:04:56.735738Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation c40d23b7-eece-4928-99e9-19d3e0d190cf · outbound

This paper cites Group Sequence Policy Optimization.

Information Gain-based Rollout Policy Optimization: An Adaptive Tree-Structured Rollout Approach for Multi-Turn LLM Agents Group Sequence Policy Optimization

Reference 22

Resolution: verified exact; local_arxiv, observed 2026-07-08T13:04:56.706723Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 76a4e2d2-bb8f-4746-9e5b-1a996670f524 · outbound

This paper cites ZeroSearch: Incentivize the Search Capability of LLMs without Searching.

Information Gain-based Rollout Policy Optimization: An Adaptive Tree-Structured Rollout Approach for Multi-Turn LLM Agents ZeroSearch: Incentivize the Search Capability of LLMs without Searching

Reference 23

Resolution: verified exact; local_arxiv, observed 2026-07-08T13:04:56.726732Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation fbd51867-5c1e-427a-970d-6080a2d3ed18 · outbound

This paper cites R1-Searcher: Incentivizing the Search Capability in LLMs via Reinforcement Learning.

Information Gain-based Rollout Policy Optimization: An Adaptive Tree-Structured Rollout Approach for Multi-Turn LLM Agents R1-Searcher: Incentivizing the Search Capability in LLMs via Reinforcement Learning

Reference 24

Resolution: verified exact; local_arxiv, observed 2026-07-08T13:04:56.732119Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 40f437aa-3049-4bf9-8921-3c39e18ee3c7 · outbound

This paper cites Smart-searcher: Incentivizing the dynamic knowledge acquisition of llms via reinforcement learning.

Information Gain-based Rollout Policy Optimization: An Adaptive Tree-Structured Rollout Approach for Multi-Turn LLM Agents Smart-searcher: Incentivizing the dynamic knowledge acquisition of llms via reinforcement learning

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-07-08T13:04:57.073965Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 733fe686-4d61-4648-8494-88de7018c776 · outbound

This paper cites StepSearch: Igniting LLMs Search Ability via Step-Wise Proximal Policy Optimization.

Information Gain-based Rollout Policy Optimization: An Adaptive Tree-Structured Rollout Approach for Multi-Turn LLM Agents StepSearch: Igniting LLMs Search Ability via Step-Wise Proximal Policy Optimization

Reference 26

Resolution: verified exact; local_arxiv, observed 2026-07-08T13:04:56.729725Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 9641d324-553f-4063-8ade-d0947055e109 · outbound

This paper cites Agentic Reinforced Policy Optimization.

Information Gain-based Rollout Policy Optimization: An Adaptive Tree-Structured Rollout Approach for Multi-Turn LLM Agents Agentic Reinforced Policy Optimization

Reference 27

Resolution: verified exact; local_arxiv, observed 2026-07-08T13:04:56.693393Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation b46cd4e0-7718-4392-829a-35b24d6e2297 · outbound

This paper cites Natural questions: a benchmark for question answering research.Transactions of the Association for Computational Linguistics, 7:453–466.

Information Gain-based Rollout Policy Optimization: An Adaptive Tree-Structured Rollout Approach for Multi-Turn LLM Agents Natural questions: a benchmark for question answering research.Transactions of the Association for Computational Linguistics, 7:453–466

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-07-08T13:04:57.089868Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 7a7ae40f-1efd-4503-abf7-7c5332196998 · outbound

This paper cites Triviaqa: A large scale distantly supervised challenge dataset for reading comprehension.

Information Gain-based Rollout Policy Optimization: An Adaptive Tree-Structured Rollout Approach for Multi-Turn LLM Agents Triviaqa: A large scale distantly supervised challenge dataset for reading comprehension

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-07-08T13:04:57.076213Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 71e13e9f-c272-4e1e-a83f-32eee4a60b52 · outbound

This paper cites When not to trust language models: Investigating effectiveness of parametric and non-parametric memories.

Information Gain-based Rollout Policy Optimization: An Adaptive Tree-Structured Rollout Approach for Multi-Turn LLM Agents When not to trust language models: Investigating effectiveness of parametric and non-parametric memories

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-07-08T13:04:57.081101Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 588a4f76-4c1f-496c-8435-803fdfe81971 · outbound

This paper cites Hotpotqa: A dataset for diverse, explainable multi-hop question answering.

Information Gain-based Rollout Policy Optimization: An Adaptive Tree-Structured Rollout Approach for Multi-Turn LLM Agents Hotpotqa: A dataset for diverse, explainable multi-hop question answering

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-07-08T13:04:57.072005Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation ca273a76-9e6c-4df2-abef-9dd2c4417ae1 · outbound

This paper cites Constructing a multi-hop qa dataset for comprehensive evaluation of reasoning steps.

Information Gain-based Rollout Policy Optimization: An Adaptive Tree-Structured Rollout Approach for Multi-Turn LLM Agents Constructing a multi-hop qa dataset for comprehensive evaluation of reasoning steps

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-07-08T13:04:57.069900Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation d7a05ff2-e2b9-45ef-bf4f-34290dfa4c1b · outbound

This paper cites Musique: Multihop questions via single-hop question composition.Transactions of the Association for Computational Linguistics, 10:539–554.

Information Gain-based Rollout Policy Optimization: An Adaptive Tree-Structured Rollout Approach for Multi-Turn LLM Agents Musique: Multihop questions via single-hop question composition.Transactions of the Association for Computational Linguistics, 10:539–554

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-07-08T13:04:57.063647Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 95cbde69-9b32-4dbe-af78-721da3d53d77 · outbound

This paper cites Measuring and narrowing the compositionality gap in language models.

Information Gain-based Rollout Policy Optimization: An Adaptive Tree-Structured Rollout Approach for Multi-Turn LLM Agents Measuring and narrowing the compositionality gap in language models

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-07-08T13:04:57.066034Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation e92f7f51-20c9-40bc-81e6-3d8406a88775 · outbound

This paper cites an unresolved cited work.

Information Gain-based Rollout Policy Optimization: An Adaptive Tree-Structured Rollout Approach for Multi-Turn LLM Agents Unresolved cited work

Reference 35

Resolution: unresolved; raw_fallback, observed 2026-07-08T13:04:57.067705Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 76e253ca-14b7-44a0-ab5e-51ed8aa1c835 · outbound

This paper cites Text Embeddings by Weakly-Supervised Contrastive Pre-training.

Information Gain-based Rollout Policy Optimization: An Adaptive Tree-Structured Rollout Approach for Multi-Turn LLM Agents Text Embeddings by Weakly-Supervised Contrastive Pre-training

Reference 36

Resolution: verified exact; local_arxiv, observed 2026-07-08T13:04:56.699304Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 7b48ea87-c656-4864-8393-c46097a58a1b · outbound

This paper cites Asymptotic evaluation of certain markov process expectations for large time, i.Communications on pure and applied mathematics, 28 (1):1–47, 1975.

Information Gain-based Rollout Policy Optimization: An Adaptive Tree-Structured Rollout Approach for Multi-Turn LLM Agents Asymptotic evaluation of certain markov process expectations for large time, i.Communications on pure and applied mathematics, 28 (1):1–47, 1975

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-07-08T13:04:57.079064Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Pith citing papers

Observation 601c01fc-f827-46be-80ab-f4ef28a8bd95 · inbound

Beyond the Mean: Multi-Moment Policy Optimization for LLM Reasoning cites this paper.

Beyond the Mean: Multi-Moment Policy Optimization for LLM Reasoning Information Gain-based Rollout Policy Optimization: An Adaptive Tree-Structured Rollout Approach for Multi-Turn LLM Agents

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-04T13:44:42.254343Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 454f7f37-0091-4243-aba9-34b22322ce7d · inbound

Optimizing What Policies Learn From: Recoverability-aware Rollout Intervention Learning cites this paper.

Optimizing What Policies Learn From: Recoverability-aware Rollout Intervention Learning Information Gain-based Rollout Policy Optimization: An Adaptive Tree-Structured Rollout Approach for Multi-Turn LLM Agents

Reference 20

Resolution: verified exact; local_arxiv, observed 2026-08-06T05:53:52.858284Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.