Paper Citation Record · LEDGER

Reinforcement Learning for Machine Learning Engineering Agents

As of 15 August 2026, this Paper Citation Record lists 62 of 62 outbound references and 7 inbound Pith citation observations for arXiv:2509.01684.

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

pith.paper-citation-record.v1
2509.01684 v1

Coverage vector

measured 62 of 62 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-05T12:24:02.385113Z

measured 69 of 69 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 7 of 7 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-07-31T01:39:49.218941Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-01T13:25:45.948001Z

Reference resolution

62 of 62 outbound references displayed

verified exact3
verified fuzzy10
unresolved49
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 4bf46cd9-754d-4002-aa2b-02ba6e56517d · outbound

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

Reinforcement Learning for Machine Learning Engineering Agents SWE-bench: Can Language Models Resolve Real-World GitHub Issues?

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-05T12:24:02.221626Z

Source-reported events for the cited work

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Observation 144bf1fe-35c3-4062-83d3-ac95553d6215 · outbound

This paper cites Swe-agent: Agent-computer interfaces enable automated software engineering.

Reinforcement Learning for Machine Learning Engineering Agents Swe-agent: Agent-computer interfaces enable automated software engineering

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-05T12:24:02.225364Z

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Observation 594ff46b-6c87-4ac0-8d3a-9f4b2bdd3cc8 · outbound

This paper cites ResearchAgent: Iterative Research Idea Generation over Scientific Literature with Large Language Models.

Reinforcement Learning for Machine Learning Engineering Agents ResearchAgent: Iterative Research Idea Generation over Scientific Literature with Large Language Models

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-05T12:24:02.228568Z

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Observation 47afa8af-5f77-41d0-8d91-daac13edd68a · outbound

This paper cites MLAgentBench: Evaluating Language Agents on Machine Learning Experimentation.

Reinforcement Learning for Machine Learning Engineering Agents MLAgentBench: Evaluating Language Agents on Machine Learning Experimentation

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-05T12:24:02.231780Z

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Observation 9966b5d1-224c-4fea-9a94-78f61bc54f1f · outbound

This paper cites MLE-bench: Evaluating Machine Learning Agents on Machine Learning Engineering.

Reinforcement Learning for Machine Learning Engineering Agents MLE-bench: Evaluating Machine Learning Agents on Machine Learning Engineering

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-05T12:24:02.235163Z

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Observation 9576ef01-37db-4382-b092-150327a1b092 · outbound

This paper cites Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters.

Reinforcement Learning for Machine Learning Engineering Agents Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-05T12:24:02.238210Z

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Observation c020786e-7f1e-4d7b-8ad6-9f2aee623858 · outbound

This paper cites Inference Scaling Laws: An Empirical Analysis of Compute-Optimal Inference for Problem-Solving with Language Models.

Reinforcement Learning for Machine Learning Engineering Agents Inference Scaling Laws: An Empirical Analysis of Compute-Optimal Inference for Problem-Solving with Language Models

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-05T12:24:02.241388Z

Source-reported events for the cited work

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Observation 1f2ffa2e-60cf-4c26-9e4a-77610ae19182 · outbound

This paper cites Reinforcement learning: An introduction, volume 1.

Reinforcement Learning for Machine Learning Engineering Agents Reinforcement learning: An introduction, volume 1

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-05T12:24:02.244194Z

Source-reported events for the cited work

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Observation 0e0cd02a-24e6-4518-85a8-972d9b855e2a · outbound

This paper cites Qwen2.5 Technical Report.

Reinforcement Learning for Machine Learning Engineering Agents Qwen2.5 Technical Report

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-05T12:24:02.246946Z

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Observation 901e5f44-53a8-4c0b-96f4-3e9d5675e960 · outbound

This paper cites Markov decision processes: discrete stochastic dynamic programming.

Reinforcement Learning for Machine Learning Engineering Agents Markov decision processes: discrete stochastic dynamic programming

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-05T12:24:02.249817Z

Source-reported events for the cited work

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Observation 9383b62b-6c1f-4e93-858a-e3c44dd157c0 · outbound

This paper cites Simple statistical gradient-following algorithms for connectionist reinforce- ment learning.

Reinforcement Learning for Machine Learning Engineering Agents Simple statistical gradient-following algorithms for connectionist reinforce- ment learning

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T12:24:03.118040Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation d2c05991-2083-4e0e-b769-9c67a13b0e83 · outbound

This paper cites Proximal Policy Optimization Algorithms.

Reinforcement Learning for Machine Learning Engineering Agents Proximal Policy Optimization Algorithms

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-05T12:24:02.254998Z

Source-reported events for the cited work

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Observation ce8144c7-d782-4342-ba92-85ec442ee0c6 · outbound

This paper cites Efficient exploration in reinforcement learning.

Reinforcement Learning for Machine Learning Engineering Agents Efficient exploration in reinforcement learning

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T12:24:03.109462Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation df3c1527-bd08-4cde-9319-751b1c54317c · outbound

This paper cites On the sample complexity of reinforcement learning.

Reinforcement Learning for Machine Learning Engineering Agents On the sample complexity of reinforcement learning

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T12:24:03.101100Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 23b6d3c8-25cc-4ef7-bd32-6497a846884b · outbound

This paper cites Rllib: Abstractions for distributed reinforcement learning.

Reinforcement Learning for Machine Learning Engineering Agents Rllib: Abstractions for distributed reinforcement learning

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T12:24:03.092922Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 3e505bb6-a99a-4cf9-9558-1c793b2d0777 · outbound

This paper cites Acme: A Research Framework for Distributed Reinforcement Learning.

Reinforcement Learning for Machine Learning Engineering Agents Acme: A Research Framework for Distributed Reinforcement Learning

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-05T12:24:02.264694Z

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Observation ebc7e94f-e431-4133-bf0c-d2d9da6c37a8 · outbound

This paper cites HybridFlow: A Flexible and Efficient RLHF Framework.

Reinforcement Learning for Machine Learning Engineering Agents HybridFlow: A Flexible and Efficient RLHF Framework

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-05T12:24:02.267302Z

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Observation c69dd7fe-66c9-4769-9702-7e26521eac41 · outbound

This paper cites Openhands: An open platform for ai software developers as generalist agents.

Reinforcement Learning for Machine Learning Engineering Agents Openhands: An open platform for ai software developers as generalist agents

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-05T12:24:02.270253Z

Source-reported events for the cited work

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Observation 87eb226d-023f-4db7-a7d6-41b1764864f8 · outbound

This paper cites LangChain, October 2022.

Reinforcement Learning for Machine Learning Engineering Agents LangChain, October 2022

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T12:24:03.078785Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation f032b7bd-e628-47bf-871d-e3551d297aa3 · outbound

This paper cites an unresolved cited work.

Reinforcement Learning for Machine Learning Engineering Agents Unresolved cited work

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-05T12:24:02.274949Z

Source-reported events for the cited work

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Observation 744c909d-1d45-4ef6-aa4d-29e09ab1a074 · outbound

This paper cites ML-Bench: Evaluating Large Language Models and Agents for Machine Learning Tasks on Repository-Level Code.

Reinforcement Learning for Machine Learning Engineering Agents ML-Bench: Evaluating Large Language Models and Agents for Machine Learning Tasks on Repository-Level Code

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-05T12:24:02.277658Z

Source-reported events for the cited work

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Observation bbc19c93-e2ff-42b8-b25e-cd50ec355bb9 · outbound

This paper cites AutoKaggle: A Multi-Agent Framework for Autonomous Data Science Competitions.

Reinforcement Learning for Machine Learning Engineering Agents AutoKaggle: A Multi-Agent Framework for Autonomous Data Science Competitions

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-05T12:24:02.281057Z

Source-reported events for the cited work

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Observation 61707d3e-b884-480a-81fa-d9330985d6e4 · outbound

This paper cites Mlrc-bench: Can language agents solve machine learning research challenges? arXiv preprint arXiv:2504.09702, 2025.

Reinforcement Learning for Machine Learning Engineering Agents Mlrc-bench: Can language agents solve machine learning research challenges? arXiv preprint arXiv:2504.09702, 2025

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-05T12:24:02.284049Z

Source-reported events for the cited work

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Observation 526feaf1-92f5-44d8-b182-03db1f964a93 · outbound

This paper cites Large language models orchestrating structured reasoning achieve kaggle grandmaster level.

Reinforcement Learning for Machine Learning Engineering Agents Large language models orchestrating structured reasoning achieve kaggle grandmaster level

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-05T12:24:02.286622Z

Source-reported events for the cited work

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Observation b49b0a67-8f9d-4605-8bc0-3af9bf940daa · outbound

This paper cites Exploring LLM Agents for Cleaning Tabular Machine Learning Datasets.

Reinforcement Learning for Machine Learning Engineering Agents Exploring LLM Agents for Cleaning Tabular Machine Learning Datasets

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-05T12:24:02.289207Z

Source-reported events for the cited work

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Observation 4ad69974-7d04-4d41-9bf4-127080bd50a7 · outbound

This paper cites HardML: A Benchmark For Evaluating Data Science And Machine Learning knowledge and reasoning in AI.

Reinforcement Learning for Machine Learning Engineering Agents HardML: A Benchmark For Evaluating Data Science And Machine Learning knowledge and reasoning in AI

Reference 26

Resolution: verified exact; local_arxiv, observed 2026-08-05T12:24:02.725488Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation e5ce4061-220c-472e-a887-5c0ffc568eca · outbound

This paper cites AutoML-GPT: Automatic Machine Learning with GPT.

Reinforcement Learning for Machine Learning Engineering Agents AutoML-GPT: Automatic Machine Learning with GPT

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-05T12:24:02.294801Z

Source-reported events for the cited work

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Observation f59c9e87-c56e-446c-ab10-a4a91451733a · outbound

This paper cites Large Language Model Agent for Hyper-Parameter Optimization.

Reinforcement Learning for Machine Learning Engineering Agents Large Language Model Agent for Hyper-Parameter Optimization

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-05T12:24:02.297408Z

Source-reported events for the cited work

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Observation 33638af2-a3fa-4291-bb47-147f2d248376 · outbound

This paper cites Large Language Models for Constructing and Optimizing Machine Learning Workflows: A Survey.

Reinforcement Learning for Machine Learning Engineering Agents Large Language Models for Constructing and Optimizing Machine Learning Workflows: A Survey

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-05T12:24:02.299995Z

Source-reported events for the cited work

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Observation 1bf3b52d-1d92-4dc9-99c0-deaf8c7e7ba6 · outbound

This paper cites AIDE: AI-Driven Exploration in the Space of Code.

Reinforcement Learning for Machine Learning Engineering Agents AIDE: AI-Driven Exploration in the Space of Code

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-05T12:24:02.302689Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 32b4121b-5f6d-406c-a8f9-763cddf1b368 · outbound

This paper cites I-mcts: Enhancing agentic automl via introspective monte carlo tree search.

Reinforcement Learning for Machine Learning Engineering Agents I-mcts: Enhancing agentic automl via introspective monte carlo tree search

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-05T12:24:02.305241Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f69bf019-b73c-4d00-bfab-c8cbadbbab01 · outbound

This paper cites Large Language Models Cannot Self-Correct Reasoning Yet.

Reinforcement Learning for Machine Learning Engineering Agents Large Language Models Cannot Self-Correct Reasoning Yet

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-05T12:24:02.307581Z

Source-reported events for the cited work

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Observation f040e31f-271f-434e-905f-a6f0fe38d6ab · outbound

This paper cites What Did I Do Wrong? Quantifying LLMs' Sensitivity and Consistency to Prompt Engineering.

Reinforcement Learning for Machine Learning Engineering Agents What Did I Do Wrong? Quantifying LLMs' Sensitivity and Consistency to Prompt Engineering

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-05T12:24:02.310282Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 47208247-8f1c-4afa-a06e-06c1ced3c7b3 · outbound

This paper cites Policy gradient meth- ods for reinforcement learning with function approximation.

Reinforcement Learning for Machine Learning Engineering Agents Policy gradient meth- ods for reinforcement learning with function approximation

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T12:24:03.063124Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation b4c2fccd-badd-4374-bad3-4d06a283d41d · outbound

This paper cites A natural policy gradient.

Reinforcement Learning for Machine Learning Engineering Agents A natural policy gradient

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-05T12:24:02.315451Z

Source-reported events for the cited work

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Observation 2ce322a9-b301-4171-9f00-edee91f7b9eb · outbound

This paper cites Trust region policy optimization.

Reinforcement Learning for Machine Learning Engineering Agents Trust region policy optimization

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-05T12:24:02.317898Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9c921657-35d1-4b7f-9254-1fb19008fabf · outbound

This paper cites Q-learning.

Reinforcement Learning for Machine Learning Engineering Agents Q-learning

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-05T12:24:02.320421Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 01289653-dd89-4bdf-b7b1-b015b37a0994 · outbound

This paper cites Mujoco: A physics engine for model-based control.

Reinforcement Learning for Machine Learning Engineering Agents Mujoco: A physics engine for model-based control

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-05T12:24:02.322903Z

Source-reported events for the cited work

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Observation c862a379-db9e-4500-a98d-c075fb028046 · outbound

This paper cites The arcade learning environment: An evaluation platform for general agents.

Reinforcement Learning for Machine Learning Engineering Agents The arcade learning environment: An evaluation platform for general agents

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-05T12:24:02.325271Z

Source-reported events for the cited work

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Observation 5ff97f97-2147-4292-8299-ebd823436c31 · outbound

This paper cites TensorFlow Agents: Efficient Batched Reinforcement Learning in TensorFlow.

Reinforcement Learning for Machine Learning Engineering Agents TensorFlow Agents: Efficient Batched Reinforcement Learning in TensorFlow

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-05T12:24:02.327658Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0789f35c-5da4-4f0e-98f8-939f45184155 · outbound

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

Reinforcement Learning for Machine Learning Engineering Agents Training language models to follow instructions with human feedback

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-05T12:24:02.330223Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e821198e-723f-440c-a807-9c844497fc6a · outbound

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

Reinforcement Learning for Machine Learning Engineering Agents Direct preference optimization: Your language model is secretly a reward model

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-05T12:24:02.332833Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fba52266-aafe-477b-87f4-e98595bc8d66 · outbound

This paper cites Brown, Miljan Martic, Shane Legg, and Dario Amodei.

Reinforcement Learning for Machine Learning Engineering Agents Brown, Miljan Martic, Shane Legg, and Dario Amodei

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-05T12:24:02.335340Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0c9982e0-0b5c-44a5-846c-e75b2aa4f9eb · outbound

This paper cites Ziegler, Nisan Stiennon, Jeffrey Wu, Tom B.

Reinforcement Learning for Machine Learning Engineering Agents Ziegler, Nisan Stiennon, Jeffrey Wu, Tom B

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-05T12:24:02.337694Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 791f7e20-5df9-4eac-bc45-a78e5bbeb58e · outbound

This paper cites Rlaif: Scaling reinforcement learning from human feedback with ai feedback.

Reinforcement Learning for Machine Learning Engineering Agents Rlaif: Scaling reinforcement learning from human feedback with ai feedback

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-05T12:24:02.340124Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 110fa51b-0c9e-426f-aed5-b3fd2a2f26c8 · outbound

This paper cites Reinforcement learning for reasoning in small llms: What works and what doesn’t.

Reinforcement Learning for Machine Learning Engineering Agents Reinforcement learning for reasoning in small llms: What works and what doesn’t

Reference 46

Resolution: unresolved; no resolver link, observed 2026-08-05T12:24:02.342410Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a9be106b-4916-4424-b1d1-011921a36fee · outbound

This paper cites SWE-RL: Advancing LLM Reasoning via Reinforcement Learning on Open Software Evolution.

Reinforcement Learning for Machine Learning Engineering Agents SWE-RL: Advancing LLM Reasoning via Reinforcement Learning on Open Software Evolution

Reference 47

Resolution: unresolved; no resolver link, observed 2026-08-05T12:24:02.344754Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8d4775dd-103e-4381-b55a-ae26a0d00da0 · outbound

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

Reinforcement Learning for Machine Learning Engineering Agents DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Reference 48

Resolution: unresolved; no resolver link, observed 2026-08-05T12:24:02.347414Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 054e1061-806e-475b-af2f-b24adf327aa1 · outbound

This paper cites Agentbench: Evaluating llms as agents, 2023.

Reinforcement Learning for Machine Learning Engineering Agents Agentbench: Evaluating llms as agents, 2023

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T12:24:02.995813Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation f725624d-fd98-4f03-9ba1-766c3fe32d60 · outbound

This paper cites Context-Aware Language Modeling for Goal-Oriented Dialogue Systems.

Reinforcement Learning for Machine Learning Engineering Agents Context-Aware Language Modeling for Goal-Oriented Dialogue Systems

Reference 50

Resolution: verified exact; local_arxiv, observed 2026-08-05T12:24:02.492803Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 6d631304-f8d8-4a31-bb62-83143c552e6e · outbound

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

Reinforcement Learning for Machine Learning Engineering Agents Digirl: Training in-the-wild device-control agents with autonomous reinforcement learning

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T12:24:02.987270Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation c4b31ec5-ca97-4e35-a30f-4380ee3cba57 · outbound

This paper cites CHAI: A CHatbot AI for Task-Oriented Dialogue with Offline Reinforcement Learning.

Reinforcement Learning for Machine Learning Engineering Agents CHAI: A CHatbot AI for Task-Oriented Dialogue with Offline Reinforcement Learning

Reference 52

Resolution: unresolved; no resolver link, observed 2026-08-05T12:24:02.358017Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e560e437-4891-4104-bbe9-cfd7031438cb · outbound

This paper cites Xu, Hao Zhu, Xuhui Zhou, Robert Lo, Abishek Sridhar, Xianyi Cheng, Tianyue Ou, Yonatan Bisk, Daniel Fried and Uri Alon, and Graham Neubig.

Reinforcement Learning for Machine Learning Engineering Agents Xu, Hao Zhu, Xuhui Zhou, Robert Lo, Abishek Sridhar, Xianyi Cheng, Tianyue Ou, Yonatan Bisk, Daniel Fried and Uri Alon, and Graham Neubig

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T12:24:02.978372Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation a9c7e422-c2ae-4624-8714-edd191db63da · outbound

This paper cites Language Understanding for Text-based Games Using Deep Reinforcement Learning.

Reinforcement Learning for Machine Learning Engineering Agents Language Understanding for Text-based Games Using Deep Reinforcement Learning

Reference 54

Resolution: verified exact; local_arxiv, observed 2026-08-05T12:24:02.472444Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation bd9f13a9-47f1-4a00-b732-6889e6f75555 · outbound

This paper cites Offline RL for Natural Language Generation with Implicit Language Q Learning.

Reinforcement Learning for Machine Learning Engineering Agents Offline RL for Natural Language Generation with Implicit Language Q Learning

Reference 55

Resolution: unresolved; no resolver link, observed 2026-08-05T12:24:02.365511Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ef0ee11a-e246-440f-8cbe-5c3556f6446b · outbound

This paper cites Process Reward Models for LLM Agents: Practical Framework and Directions.

Reinforcement Learning for Machine Learning Engineering Agents Process Reward Models for LLM Agents: Practical Framework and Directions

Reference 56

Resolution: unresolved; no resolver link, observed 2026-08-05T12:24:02.368310Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 86d3afb9-4361-46b8-b3d1-ff99a13bd789 · outbound

This paper cites Generative Verifiers: Reward Modeling as Next-Token Prediction.

Reinforcement Learning for Machine Learning Engineering Agents Generative Verifiers: Reward Modeling as Next-Token Prediction

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-05T12:24:02.371405Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation afebba1c-3ec7-4a55-b95c-cf857a34b4f3 · outbound

This paper cites Generative Reward Models.

Reinforcement Learning for Machine Learning Engineering Agents Generative Reward Models

Reference 58

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Observation b270066d-cec2-4cf4-9af0-0983af8ac565 · outbound

This paper cites Autonomous Evaluation and Refinement of Digital Agents.

Reinforcement Learning for Machine Learning Engineering Agents Autonomous Evaluation and Refinement of Digital Agents

Reference 59

Resolution: unresolved; no resolver link, observed 2026-08-05T12:24:02.377155Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5ef0f259-b734-480b-83f0-32aa46ffb8de · outbound

This paper cites Code as Reward: Empowering Reinforcement Learning with VLMs.

Reinforcement Learning for Machine Learning Engineering Agents Code as Reward: Empowering Reinforcement Learning with VLMs

Reference 60

Resolution: unresolved; no resolver link, observed 2026-08-05T12:24:02.379856Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a27a564b-280b-4a49-950f-36f9344456a2 · outbound

This paper cites LLM-as-a-Judge & Reward Model: What They Can and Cannot Do.

Reinforcement Learning for Machine Learning Engineering Agents LLM-as-a-Judge & Reward Model: What They Can and Cannot Do

Reference 61

Resolution: unresolved; no resolver link, observed 2026-08-05T12:24:02.382469Z

Source-reported events for the cited work

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Observation d6fa3e54-ec90-476f-bda8-0103cde99a26 · outbound

This paper cites ‘ re- quest_id,requester_received_pizza t3_i8iy4,0 t3_1mfqi0,0 etc “‘ • Data snippet: -> /workdir/random-acts-of-pizza/prepared/public/test.json: [.

Reinforcement Learning for Machine Learning Engineering Agents ‘ re- quest_id,requester_received_pizza t3_i8iy4,0 t3_1mfqi0,0 etc “‘ • Data snippet: -> /workdir/random-acts-of-pizza/prepared/public/test.json: [

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T12:24:02.969875Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Pith citing papers

Observation b3137228-5778-4160-88a4-893fd1c75603 · inbound

PACEvolve++: Improving Test-time Learning for Evolutionary Search Agents cites this paper.

PACEvolve++: Improving Test-time Learning for Evolutionary Search Agents Reinforcement Learning for Machine Learning Engineering Agents

Reference 47

Resolution: verified exact; arxiv_id, observed 2026-05-11T05:00:56.352524Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 5630521f-8c37-4617-a14a-a9b49de7a90b · inbound

MLS-Bench: A Holistic and Rigorous Assessment of AI Systems on Building Better AI cites this paper.

MLS-Bench: A Holistic and Rigorous Assessment of AI Systems on Building Better AI Reinforcement Learning for Machine Learning Engineering Agents

Reference 114

Resolution: verified exact; arxiv_id, observed 2026-05-12T08:21:25.286968Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 57a77419-c52c-4371-a291-6d2b39a723ff · inbound

MLS-Bench: A Holistic and Rigorous Assessment of AI Systems on Building Better AI cites this paper.

MLS-Bench: A Holistic and Rigorous Assessment of AI Systems on Building Better AI Reinforcement Learning for Machine Learning Engineering Agents

Reference 116

Resolution: verified exact; arxiv_id, observed 2026-07-01T13:25:45.958176Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 86ff746c-ce33-464e-926e-0bee3a1a5c7f · inbound

MLS-Bench: A Holistic and Rigorous Assessment of AI Systems on Building Better AI cites this paper.

MLS-Bench: A Holistic and Rigorous Assessment of AI Systems on Building Better AI Reinforcement Learning for Machine Learning Engineering Agents

Reference 115

Resolution: unresolved; no resolver link, observed 2026-07-12T17:14:49.310598Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 83d2cc52-cd90-48ae-a519-dba65306dc3c · inbound

Revisiting DAgger in the Era of LLM-Agents cites this paper.

Revisiting DAgger in the Era of LLM-Agents Reinforcement Learning for Machine Learning Engineering Agents

Reference 42

Resolution: verified exact; arxiv_id, observed 2026-05-14T19:57:53.418198Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 082a7f23-a38a-4140-b7dd-43e98d831d0b · inbound

Reinforcement Learning with Verifiable Physics: Post-training LLMs with Continuous Rewards cites this paper.

Reinforcement Learning with Verifiable Physics: Post-training LLMs with Continuous Rewards Reinforcement Learning for Machine Learning Engineering Agents

Reference 40

Resolution: unresolved; no resolver link, observed 2026-07-14T11:28:23.511747Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b223f18e-0fb4-47d0-86b2-c12b00bf2c00 · inbound

Matryoshka Agent: Unfolding Sub-Agents for Long-Horizon Machine Learning Engineering cites this paper.

Matryoshka Agent: Unfolding Sub-Agents for Long-Horizon Machine Learning Engineering Reinforcement Learning for Machine Learning Engineering Agents

Reference 56

Resolution: unresolved; no resolver link, observed 2026-07-31T01:39:49.218941Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.