Paper Citation Record · LEDGER

EvoTrainer: Co-Evolving LLM Policies and Training Harnesses for Autonomous Agentic Reinforcement Learning

As of 16 August 2026, this Paper Citation Record lists 45 of 45 outbound references and 1 inbound Pith citation observation for arXiv:2606.03108.

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

pith.paper-citation-record.v1
2606.03108 v1

Coverage vector

measured 45 of 45 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-06-28T10:30:42.057301Z

measured 46 of 46 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-15T06:32:42.880941+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-02T04:32:38.114748Z

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

45 of 45 outbound references displayed

verified exact30
verified fuzzy0
unresolved14
parse uncertain0
malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation a7ab1db5-7afa-4111-bc66-8c630582546d · outbound

This paper cites 2026 , howpublished =.

EvoTrainer: Co-Evolving LLM Policies and Training Harnesses for Autonomous Agentic Reinforcement Learning 2026 , howpublished =

Reference 1

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Observation 9aa53133-e85e-46b9-af69-3793cccce76b · outbound

This paper cites Darwin G.

EvoTrainer: Co-Evolving LLM Policies and Training Harnesses for Autonomous Agentic Reinforcement Learning Darwin G

Reference 7

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EvoTrainer: Co-Evolving LLM Policies and Training Harnesses for Autonomous Agentic Reinforcement Learning Unresolved cited work

Reference 15

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EvoTrainer: Co-Evolving LLM Policies and Training Harnesses for Autonomous Agentic Reinforcement Learning Unresolved cited work

Reference 22

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EvoTrainer: Co-Evolving LLM Policies and Training Harnesses for Autonomous Agentic Reinforcement Learning Unresolved cited work

Reference 30

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EvoTrainer: Co-Evolving LLM Policies and Training Harnesses for Autonomous Agentic Reinforcement Learning Unresolved cited work

Reference 31

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Observation c80d199f-6e67-43ff-bece-84f5dbc9f20c · outbound

This paper cites Advances in Neural Information Processing Systems (.

EvoTrainer: Co-Evolving LLM Policies and Training Harnesses for Autonomous Agentic Reinforcement Learning Advances in Neural Information Processing Systems (

Reference 32

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Observation 8bd2eec7-180a-44ef-bd43-7e7037dad973 · outbound

This paper cites Proceedings of the Thirty-Second.

EvoTrainer: Co-Evolving LLM Policies and Training Harnesses for Autonomous Agentic Reinforcement Learning Proceedings of the Thirty-Second

Reference 33

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EvoTrainer: Co-Evolving LLM Policies and Training Harnesses for Autonomous Agentic Reinforcement Learning 2026 , eprint =

Reference 35

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Observation 9f438de2-b65b-4608-b838-150e6ccc60d8 · outbound

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EvoTrainer: Co-Evolving LLM Policies and Training Harnesses for Autonomous Agentic Reinforcement Learning Bellemare

Reference 36

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This paper cites Big-Math: A Large-Scale, High-Quality Math Dataset for Reinforcement Learning in Language Models.

EvoTrainer: Co-Evolving LLM Policies and Training Harnesses for Autonomous Agentic Reinforcement Learning Big-Math: A Large-Scale, High-Quality Math Dataset for Reinforcement Learning in Language Models

Reference 37

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EvoTrainer: Co-Evolving LLM Policies and Training Harnesses for Autonomous Agentic Reinforcement Learning Unresolved cited work

Reference 38

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Observation 4f7bcaba-cd9d-469a-b9c1-8cade883d28d · outbound

This paper cites SWE-rebench: An Automated Pipeline for Task Collection and Decontaminated Evaluation of Software Engineering Agents.

EvoTrainer: Co-Evolving LLM Policies and Training Harnesses for Autonomous Agentic Reinforcement Learning SWE-rebench: An Automated Pipeline for Task Collection and Decontaminated Evaluation of Software Engineering Agents

Reference 39

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EvoTrainer: Co-Evolving LLM Policies and Training Harnesses for Autonomous Agentic Reinforcement Learning Unresolved cited work

Reference 40

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This paper cites Reinforcement Learning for Long-Horizon Interactive LLM Agents.

EvoTrainer: Co-Evolving LLM Policies and Training Harnesses for Autonomous Agentic Reinforcement Learning Reinforcement Learning for Long-Horizon Interactive LLM Agents

Reference 41

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Observation df0da249-623f-44ca-83e5-5bec76037bda · outbound

This paper cites arXiv preprint arXiv:2505.09655 , year=.

EvoTrainer: Co-Evolving LLM Policies and Training Harnesses for Autonomous Agentic Reinforcement Learning arXiv preprint arXiv:2505.09655 , year=

Reference 42

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Observation 18e13c95-ede7-4f47-8d4e-394a87fb00bf · outbound

This paper cites How Many Random Seeds? Statistical Power Analysis in Deep Reinforcement Learning Experiments.

EvoTrainer: Co-Evolving LLM Policies and Training Harnesses for Autonomous Agentic Reinforcement Learning How Many Random Seeds? Statistical Power Analysis in Deep Reinforcement Learning Experiments

Reference 43

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Observation 201448c2-adc1-4eb4-b78d-a5d8f0035b45 · outbound

This paper cites doi: 10.1038/s41586-025-09422-z.

EvoTrainer: Co-Evolving LLM Policies and Training Harnesses for Autonomous Agentic Reinforcement Learning doi: 10.1038/s41586-025-09422-z

Reference 44

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EvoTrainer: Co-Evolving LLM Policies and Training Harnesses for Autonomous Agentic Reinforcement Learning Unresolved cited work

Reference 45

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Observation 9cc9bac4-8ee7-46e7-a7d2-dc24d144180f · outbound

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EvoTrainer: Co-Evolving LLM Policies and Training Harnesses for Autonomous Agentic Reinforcement Learning R-Zero: Self-Evolving Reasoning LLM from Zero Data

Reference 46

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Observation bd8e608a-d9f9-4550-a120-4841c52efd17 · outbound

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EvoTrainer: Co-Evolving LLM Policies and Training Harnesses for Autonomous Agentic Reinforcement Learning On the Emergence of Implicit Curriculum in RLVR Learning Dynamics

Reference 47

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Observation fe3d4da6-9167-4967-aa75-8db0d155d2ba · outbound

This paper cites LiveCodeBench: Holistic and Contamination Free Evaluation of Large Language Models for Code.

EvoTrainer: Co-Evolving LLM Policies and Training Harnesses for Autonomous Agentic Reinforcement Learning LiveCodeBench: Holistic and Contamination Free Evaluation of Large Language Models for Code

Reference 48

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EvoTrainer: Co-Evolving LLM Policies and Training Harnesses for Autonomous Agentic Reinforcement Learning GEAR: Genetic AutoResearch for Agentic Code Evolution

Reference 49

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EvoTrainer: Co-Evolving LLM Policies and Training Harnesses for Autonomous Agentic Reinforcement Learning Unresolved cited work

Reference 50

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EvoTrainer: Co-Evolving LLM Policies and Training Harnesses for Autonomous Agentic Reinforcement Learning Meta-Harness: End-to-End Optimization of Model Harnesses

Reference 51

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EvoTrainer: Co-Evolving LLM Policies and Training Harnesses for Autonomous Agentic Reinforcement Learning TACO: Topics in Algorithmic COde generation dataset

Reference 52

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EvoTrainer: Co-Evolving LLM Policies and Training Harnesses for Autonomous Agentic Reinforcement Learning Agentic Harness Engineering: Observability-Driven Automatic Evolution of Coding-Agent Harnesses

Reference 53

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EvoTrainer: Co-Evolving LLM Policies and Training Harnesses for Autonomous Agentic Reinforcement Learning Understanding R1-Zero-Like Training: A Critical Perspective

Reference 54

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Observation faee7cf6-a254-4a18-9814-36bb86ca4520 · outbound

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EvoTrainer: Co-Evolving LLM Policies and Training Harnesses for Autonomous Agentic Reinforcement Learning GHPO: Adaptive Guidance for Stable and Efficient LLM Reinforcement Learning

Reference 55

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This paper cites The AI Scientist: Towards Fully Automated Open-Ended Scientific Discovery.

EvoTrainer: Co-Evolving LLM Policies and Training Harnesses for Autonomous Agentic Reinforcement Learning The AI Scientist: Towards Fully Automated Open-Ended Scientific Discovery

Reference 56

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EvoTrainer: Co-Evolving LLM Policies and Training Harnesses for Autonomous Agentic Reinforcement Learning Auto Research with Specialist Agents Develops Effective and Non-Trivial Training Recipes

Reference 57

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EvoTrainer: Co-Evolving LLM Policies and Training Harnesses for Autonomous Agentic Reinforcement Learning AlphaEvolve: A coding agent for scientific and algorithmic discovery

Reference 58

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EvoTrainer: Co-Evolving LLM Policies and Training Harnesses for Autonomous Agentic Reinforcement Learning Bilevel Autoresearch: Meta-Autoresearching Itself

Reference 59

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EvoTrainer: Co-Evolving LLM Policies and Training Harnesses for Autonomous Agentic Reinforcement Learning Unresolved cited work

Reference 60

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EvoTrainer: Co-Evolving LLM Policies and Training Harnesses for Autonomous Agentic Reinforcement Learning DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models

Reference 61

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EvoTrainer: Co-Evolving LLM Policies and Training Harnesses for Autonomous Agentic Reinforcement Learning A Survey on Self-Evolution of Large Language Models

Reference 62

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EvoTrainer: Co-Evolving LLM Policies and Training Harnesses for Autonomous Agentic Reinforcement Learning RAGEN-2: Reasoning Collapse in Agentic RL

Reference 63

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EvoTrainer: Co-Evolving LLM Policies and Training Harnesses for Autonomous Agentic Reinforcement Learning RAGEN: Understanding Self-Evolution in LLM Agents via Multi-Turn Reinforcement Learning

Reference 64

Resolution: verified exact; local_arxiv, observed 2026-07-02T02:56:29.172159Z

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Reference 65

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Observation 78483e8e-add2-4202-86b9-6f75ba1c7e46 · outbound

This paper cites The AI Scientist-v2: Workshop-Level Automated Scientific Discovery via Agentic Tree Search.

EvoTrainer: Co-Evolving LLM Policies and Training Harnesses for Autonomous Agentic Reinforcement Learning The AI Scientist-v2: Workshop-Level Automated Scientific Discovery via Agentic Tree Search

Reference 66

Resolution: verified exact; local_arxiv, observed 2026-07-02T02:56:29.137487Z

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Observation 06fe170b-1ac8-4284-bc72-4b7db3f2abdd · outbound

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

EvoTrainer: Co-Evolving LLM Policies and Training Harnesses for Autonomous Agentic Reinforcement Learning DAPO: An Open-Source LLM Reinforcement Learning System at Scale

Reference 67

Resolution: verified exact; local_arxiv, observed 2026-07-02T02:56:29.113543Z

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Observation 5bcca6bf-293b-4752-b7da-98a8dde3362c · outbound

This paper cites Easy Samples Are All You Need: Self-Evolving LLMs via Data-Efficient Reinforcement Learning.

EvoTrainer: Co-Evolving LLM Policies and Training Harnesses for Autonomous Agentic Reinforcement Learning Easy Samples Are All You Need: Self-Evolving LLMs via Data-Efficient Reinforcement Learning

Reference 68

Resolution: verified exact; local_arxiv, observed 2026-07-02T02:56:29.109678Z

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Observation 1f8654e5-305c-46fe-9781-a682b495bb4e · outbound

This paper cites RLVE: Scaling Up Reinforcement Learning for Language Models with Adaptive Verifiable Environments.

EvoTrainer: Co-Evolving LLM Policies and Training Harnesses for Autonomous Agentic Reinforcement Learning RLVE: Scaling Up Reinforcement Learning for Language Models with Adaptive Verifiable Environments

Reference 69

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Observation 148e3c70-6848-4df5-ba8e-a552e8a8b7fb · outbound

This paper cites Darwin Godel Machine: Open-Ended Evolution of Self-Improving Agents.

EvoTrainer: Co-Evolving LLM Policies and Training Harnesses for Autonomous Agentic Reinforcement Learning Darwin Godel Machine: Open-Ended Evolution of Self-Improving Agents

Reference 70

Resolution: verified exact; local_arxiv, observed 2026-07-02T02:56:29.145323Z

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EvoTrainer: Co-Evolving LLM Policies and Training Harnesses for Autonomous Agentic Reinforcement Learning Group Sequence Policy Optimization

Reference 71

Resolution: verified exact; local_arxiv, observed 2026-07-02T02:56:29.145569Z

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Pith citing papers

Observation 7ddcb646-167c-4ac7-8c40-a26e10b1623a · inbound

Self-Evolving Agent Harnesses via Gated Semantic Quality-Diversity cites this paper.

Self-Evolving Agent Harnesses via Gated Semantic Quality-Diversity EvoTrainer: Co-Evolving LLM Policies and Training Harnesses for Autonomous Agentic Reinforcement Learning

Reference 38

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