DiDPO: Diff-in-Diff Policy Optimization for Coding Agent Training | Paper Citation Record

Observation 96dd344a-c9a8-4a6c-8fe7-66aa61c3c76e · outbound

This paper cites Program Synthesis with Large Language Models.

DiDPO: Diff-in-Diff Policy Optimization for Coding Agent Training Program Synthesis with Large Language Models

Reference 1

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Observation b30ba429-aa4e-4f0c-a4a1-b7431964c91f · outbound

This paper cites A general theo- retical paradigm to understand learning from hu- man preferences.

DiDPO: Diff-in-Diff Policy Optimization for Coding Agent Training A general theo- retical paradigm to understand learning from hu- man preferences

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:00:54.891649Z

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Observation c0caab54-2410-4fa9-88d2-a451dd47fc33 · outbound

This paper cites A course in metric geometry, volume 33.

DiDPO: Diff-in-Diff Policy Optimization for Coding Agent Training A course in metric geometry, volume 33

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:00:54.868084Z

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Observation eafb808e-3f7e-4071-88ff-7ec9de1adfa6 · outbound

This paper cites MiniMax-M1: Scaling Test-Time Compute Efficiently with Lightning Attention.

DiDPO: Diff-in-Diff Policy Optimization for Coding Agent Training MiniMax-M1: Scaling Test-Time Compute Efficiently with Lightning Attention

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-10T14:00:52.729442Z

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Observation a2414384-1702-4a07-87fb-580d91a55674 · outbound

This paper cites CodeT: Code Generation with Generated Tests.

DiDPO: Diff-in-Diff Policy Optimization for Coding Agent Training CodeT: Code Generation with Generated Tests

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-10T14:00:52.742000Z

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Observation 604af2c4-39c3-4a80-8fb9-b61d552f1bdd · outbound

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

DiDPO: Diff-in-Diff Policy Optimization for Coding Agent Training Reinforcement Learning for Long-Horizon Interactive LLM Agents

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-10T14:00:52.751101Z

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Observation ee1b012a-c8f5-48a4-aed1-cd8f86d8ec9e · outbound

This paper cites Evaluating Large Language Models Trained on Code.

DiDPO: Diff-in-Diff Policy Optimization for Coding Agent Training Evaluating Large Language Models Trained on Code

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-10T14:00:52.758668Z

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Observation ded73128-d4c1-4245-9883-16835dc4b281 · outbound

This paper cites Teaching large language models to self-debug.

DiDPO: Diff-in-Diff Policy Optimization for Coding Agent Training Teaching large language models to self-debug

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:00:54.849027Z

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Observation 3e686dfd-e70d-4e72-aaf0-8c0b698f8046 · outbound

This paper cites Mem0: Building Production-Ready AI Agents with Scalable Long-Term Memory.

DiDPO: Diff-in-Diff Policy Optimization for Coding Agent Training Mem0: Building Production-Ready AI Agents with Scalable Long-Term Memory

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-10T14:00:52.774989Z

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Observation c318a7f3-178f-4734-a570-a17178156205 · outbound

This paper cites Deep reinforcement learning from human preferences.

DiDPO: Diff-in-Diff Policy Optimization for Coding Agent Training Deep reinforcement learning from human preferences

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:00:54.824505Z

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Observation a03c4c37-e90d-40b1-8b97-6a433e41c913 · outbound

This paper cites Training Verifiers to Solve Math Word Problems.

DiDPO: Diff-in-Diff Policy Optimization for Coding Agent Training Training Verifiers to Solve Math Word Problems

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-10T14:00:52.788428Z

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Observation 52f58160-bd33-4651-bd7c-c33a04f1ecc4 · outbound

This paper cites Process Reinforcement through Implicit Rewards.

DiDPO: Diff-in-Diff Policy Optimization for Coding Agent Training Process Reinforcement through Implicit Rewards

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-10T14:00:52.797382Z

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Observation c0e354f9-bf1b-46bf-aa42-b7357d9270d6 · outbound

This paper cites Memp: Exploring agent procedural memory.

DiDPO: Diff-in-Diff Policy Optimization for Coding Agent Training Memp: Exploring agent procedural memory

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:00:54.802083Z

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 bb008a4b-e7ba-45fb-beae-70e58aea9c77 · outbound

This paper cites Group-in-group policy optimization for llm agent training.

DiDPO: Diff-in-Diff Policy Optimization for Coding Agent Training Group-in-group policy optimization for llm agent training

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:00:54.781914Z

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 8a10f563-8b5a-423c-bcb6-b2ca68db2106 · outbound

This paper cites InCoder: A Generative Model for Code Infilling and Synthesis.

DiDPO: Diff-in-Diff Policy Optimization for Coding Agent Training InCoder: A Generative Model for Code Infilling and Synthesis

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-10T14:00:52.821269Z

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Observation 86773d89-d8d2-452f-b7bf-0ed4afeaf70c · outbound

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

DiDPO: Diff-in-Diff Policy Optimization for Coding Agent Training DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-10T14:00:52.827996Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3b2aedc4-624e-4d1f-be0d-6ab78e6847fc · outbound

This paper cites Skywork Open Reasoner 1 Technical Report.

DiDPO: Diff-in-Diff Policy Optimization for Coding Agent Training Skywork Open Reasoner 1 Technical Report

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-10T14:00:52.837073Z

Source-reported events for the cited work

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Observation 97a60960-3f80-4c5f-bfe0-ab41e5cb9842 · outbound

This paper cites Measuring Coding Challenge Competence With APPS.

DiDPO: Diff-in-Diff Policy Optimization for Coding Agent Training Measuring Coding Challenge Competence With APPS

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-10T14:00:52.844229Z

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Unavailable: canonical work link unavailable.

Observation 34727b05-4c6b-43c7-a075-5848c0928136 · outbound

This paper cites Cogagent: A visual language model for gui agents.

DiDPO: Diff-in-Diff Policy Optimization for Coding Agent Training Cogagent: A visual language model for gui agents

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-10T14:00:52.852500Z

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Observation 715dce2b-b515-4371-ad60-dc1057a9ebc3 · outbound

This paper cites Qwen2.5-Coder Technical Report.

DiDPO: Diff-in-Diff Policy Optimization for Coding Agent Training Qwen2.5-Coder Technical Report

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-10T14:00:52.859229Z

Source-reported events for the cited work

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Observation 07eab6a8-4ab8-441a-8f55-1c5f7d143ec6 · outbound

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

DiDPO: Diff-in-Diff Policy Optimization for Coding Agent Training LiveCodeBench: Holistic and Contamination Free Evaluation of Large Language Models for Code

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-10T14:00:52.868170Z

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Observation 72af3f08-9c54-4441-821f-77df178fe084 · outbound

This paper cites Cure: Code-aware neural machine translation for auto- matic program repair.

DiDPO: Diff-in-Diff Policy Optimization for Coding Agent Training Cure: Code-aware neural machine translation for auto- matic program repair

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:00:54.745463Z

Source-reported events for the cited work

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Observation 5d23cf88-c9c3-4622-8cf1-58faf4a36a0c · outbound

This paper cites Self-planning code generation with large language models.

DiDPO: Diff-in-Diff Policy Optimization for Coding Agent Training Self-planning code generation with large language models

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:00:54.719715Z

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 be636341-f7aa-4b45-a333-a47237e80770 · outbound

This paper cites CodeRL+: Improving Code Generation via Reinforcement with Execution Semantics Alignment.

DiDPO: Diff-in-Diff Policy Optimization for Coding Agent Training CodeRL+: Improving Code Generation via Reinforcement with Execution Semantics Alignment

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-10T14:00:52.897000Z

Source-reported events for the cited work

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Observation c30cfec5-0f4d-4ac4-a2e8-f83ee8545226 · outbound

This paper cites Swe-bench: Can language models re- solve real-world github issues? In International Conference on Learning Representations, volume 2024, pages 54107–54157, 2024.

DiDPO: Diff-in-Diff Policy Optimization for Coding Agent Training Swe-bench: Can language models re- solve real-world github issues? In International Conference on Learning Representations, volume 2024, pages 54107–54157, 2024

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:00:54.697644Z

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 1c03404b-347f-446e-9ace-e665d587bd5e · outbound

This paper cites Defects4j: A database of existing faults to enable controlled testing studies for java programs.

DiDPO: Diff-in-Diff Policy Optimization for Coding Agent Training Defects4j: A database of existing faults to enable controlled testing studies for java programs

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:00:54.677349Z

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 39751694-7240-46a2-aa05-80913ae7ff9c · outbound

This paper cites Ds- 1000: A natural and reliable benchmark for data science code generation.

DiDPO: Diff-in-Diff Policy Optimization for Coding Agent Training Ds- 1000: A natural and reliable benchmark for data science code generation

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:00:54.655863Z

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 c4df06f0-ca4c-4fbc-a9a2-8dc4f6508833 · outbound

This paper cites Coderl: Mastering code generation through pretrained mod- els and deep reinforcement learning.

DiDPO: Diff-in-Diff Policy Optimization for Coding Agent Training Coderl: Mastering code generation through pretrained mod- els and deep reinforcement learning

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:00:54.635931Z

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 b7090b3d-68ee-4154-8aa9-abadb60c460f · outbound

This paper cites A systematic study of automated program repair: Fixing 55 out of 105 bugs for $8 each.

DiDPO: Diff-in-Diff Policy Optimization for Coding Agent Training A systematic study of automated program repair: Fixing 55 out of 105 bugs for $8 each

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:00:54.613194Z

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 a4423c6f-381a-4223-ac9f-5611ff0c601e · outbound

This paper cites Meta-Harness: End-to-End Optimization of Model Harnesses.

DiDPO: Diff-in-Diff Policy Optimization for Coding Agent Training Meta-Harness: End-to-End Optimization of Model Harnesses

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-10T14:00:52.951785Z

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Observation e42778f4-00d3-49f6-8d85-2a101745a892 · outbound

This paper cites StarCoder: may the source be with you!.

DiDPO: Diff-in-Diff Policy Optimization for Coding Agent Training StarCoder: may the source be with you!

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-10T14:00:52.959772Z

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Observation a2d316f3-4a1e-459c-a37a-b2ad8723f5ba · outbound

This paper cites Let’s verify step by step.

DiDPO: Diff-in-Diff Policy Optimization for Coding Agent Training Let’s verify step by step

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:00:54.592579Z

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 d98740db-f9be-47a6-a44b-9c8a68c49c18 · outbound

This paper cites Automatic patch gen- eration by learning correct code.

DiDPO: Diff-in-Diff Policy Optimization for Coding Agent Training Automatic patch gen- eration by learning correct code

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:00:54.573032Z

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 38cc9b54-22aa-4d9e-b27b-be5d3f894c4a · outbound

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

DiDPO: Diff-in-Diff Policy Optimization for Coding Agent Training SkillClaw: Let Skills Evolve Collectively with Agentic Evolver

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-10T14:00:52.982894Z

Source-reported events for the cited work

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Observation d9e86fcb-867e-407f-ac33-e219f224351b · outbound

This paper cites Gromov–wasserstein distances and the metric approach to object matching.

DiDPO: Diff-in-Diff Policy Optimization for Coding Agent Training Gromov–wasserstein distances and the metric approach to object matching

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:00:54.553305Z

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 ffec4f66-7adb-44dd-abd4-e37dd26e4222 · outbound

This paper cites An analysis of approxima- tions for maximizing submodular set functions—i.

DiDPO: Diff-in-Diff Policy Optimization for Coding Agent Training An analysis of approxima- tions for maximizing submodular set functions—i

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:00:54.529276Z

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 c8e1f20b-cf6b-4a58-b9b5-88a3e13e7256 · outbound

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

DiDPO: Diff-in-Diff Policy Optimization for Coding Agent Training Training language models to follow instructions with human feedback

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-10T14:00:53.010831Z

Source-reported events for the cited work

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Observation 59b2863d-88c9-43e5-8ba9-a930c36d89e1 · outbound

This paper cites Direct Preference Optimization: Your Language Model is Secretly a Reward Model.

DiDPO: Diff-in-Diff Policy Optimization for Coding Agent Training Direct Preference Optimization: Your Language Model is Secretly a Reward Model

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-10T14:00:53.018708Z

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Observation 6e2f8ecb-b654-4df3-9321-66e6bf130d60 · outbound

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

DiDPO: Diff-in-Diff Policy Optimization for Coding Agent Training DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-10T14:00:53.028899Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 01e04f36-e2b7-454d-9f54-8ab530e9e8b7 · outbound

This paper cites Can Language Models Solve Olympiad Programming?.

DiDPO: Diff-in-Diff Policy Optimization for Coding Agent Training Can Language Models Solve Olympiad Programming?

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-10T14:00:53.037166Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 34648ae4-8a93-47f0-aee9-adfb3921f37d · outbound

This paper cites Reflexion: Language agents with verbal reinforcement learning.

DiDPO: Diff-in-Diff Policy Optimization for Coding Agent Training Reflexion: Language agents with verbal reinforcement learning

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-10T14:00:53.045807Z

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Observation b4ee0b14-3de4-4172-b51c-9c847a547bbc · outbound

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

DiDPO: Diff-in-Diff Policy Optimization for Coding Agent Training ALFWorld: Aligning Text and Embodied Environments for Interactive Learning

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-10T14:00:53.055761Z

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Unavailable: canonical work link unavailable.

Observation 221ec3e4-32fb-4fef-86d9-8561cf0a0d4e · outbound

This paper cites Ex- ecverify: White-box rl with verifiable stepwise re- wards for code execution reasoning.

DiDPO: Diff-in-Diff Policy Optimization for Coding Agent Training Ex- ecverify: White-box rl with verifiable stepwise re- wards for code execution reasoning

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:00:54.494879Z

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 47c4081e-8bdc-4fff-bf26-1a51e6c534a5 · outbound

This paper cites Ex- ecutable code actions elicit better llm agents.

DiDPO: Diff-in-Diff Policy Optimization for Coding Agent Training Ex- ecutable code actions elicit better llm agents

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:00:54.475860Z

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 0901c2c9-f0e5-41af-add5-75d7ad709401 · outbound

This paper cites Open- hands: An open platform for ai software developers as generalist agents.

DiDPO: Diff-in-Diff Policy Optimization for Coding Agent Training Open- hands: An open platform for ai software developers as generalist agents

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:00:54.451400Z

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 588315d6-711c-47f0-a712-50ff196738c3 · outbound

This paper cites Lightweight Self-Knowledge Distillation with Multi-source Information Fusion.

DiDPO: Diff-in-Diff Policy Optimization for Coding Agent Training Lightweight Self-Knowledge Distillation with Multi-source Information Fusion

Reference 46

Resolution: verified exact; local_arxiv, observed 2026-08-10T14:00:53.632208Z

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 e38ea274-93e9-4d8f-9c39-8e34500c770f · outbound

This paper cites Multi-label self knowledge distillation.

DiDPO: Diff-in-Diff Policy Optimization for Coding Agent Training Multi-label self knowledge distillation

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:00:54.429162Z

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 8e70c78f-71f1-41ae-9751-2a01ef3524bd · outbound

This paper cites Role-Agent: Bootstrapping LLM Agents via Dual-Role Evolution.

DiDPO: Diff-in-Diff Policy Optimization for Coding Agent Training Role-Agent: Bootstrapping LLM Agents via Dual-Role Evolution

Reference 48

Resolution: verified exact; local_arxiv, observed 2026-08-10T14:00:53.586243Z

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 4d604580-d058-465c-a1bb-13163f739bdd · outbound

This paper cites Ojbench: A competi- tion level code benchmark for large language models.

DiDPO: Diff-in-Diff Policy Optimization for Coding Agent Training Ojbench: A competi- tion level code benchmark for large language models

Reference 49

Resolution: unresolved; no resolver link, observed 2026-08-10T14:00:53.127123Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b43e4fac-d08d-4843-b78e-462087c6991e · outbound

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

DiDPO: Diff-in-Diff Policy Optimization for Coding Agent Training RAGEN: Understanding Self-Evolution in LLM Agents via Multi-Turn Reinforcement Learning

Reference 50

Resolution: unresolved; no resolver link, observed 2026-08-10T14:00:53.133261Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ca742a1c-a1c6-4dee-81a6-3d791f4b3904 · outbound

This paper cites Chain-of-thought prompting elicits reasoning in large language models.Advances in neural information processing systems, 35:24824– 24837, 2022.

DiDPO: Diff-in-Diff Policy Optimization for Coding Agent Training Chain-of-thought prompting elicits reasoning in large language models.Advances in neural information processing systems, 35:24824– 24837, 2022

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:00:54.408999Z

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 3bbe2263-4e0c-4dc1-8f32-39249168a903 · outbound

This paper cites Swe-rl: Advancing llm reasoning via reinforce- ment learning on open software evolution.Advances inNeuralInformationProcessing Systems, 38:78500– 78525, 2026.

DiDPO: Diff-in-Diff Policy Optimization for Coding Agent Training Swe-rl: Advancing llm reasoning via reinforce- ment learning on open software evolution.Advances inNeuralInformationProcessing Systems, 38:78500– 78525, 2026

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:00:54.387694Z

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 b5487387-c251-4b8f-a5bf-fcec858ac467 · outbound

This paper cites Agentless: Demystifying LLM-based Software Engineering Agents.

DiDPO: Diff-in-Diff Policy Optimization for Coding Agent Training Agentless: Demystifying LLM-based Software Engineering Agents

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-10T14:00:53.155623Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3508a87c-76e5-4c2d-8369-6cd95c41bad6 · outbound

This paper cites LeetCodeDataset: A Temporal Dataset for Robust Evaluation and Efficient Training of Code LLMs.

DiDPO: Diff-in-Diff Policy Optimization for Coding Agent Training LeetCodeDataset: A Temporal Dataset for Robust Evaluation and Efficient Training of Code LLMs

Reference 54

Resolution: unresolved; no resolver link, observed 2026-08-10T14:00:53.161982Z

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Unavailable: canonical work link unavailable.

Observation 2f320710-abfb-4221-abeb-35ea8925fee4 · outbound

This paper cites Icpc-eval: Probing the frontiers of llm reasoning with competitive pro- gramming contests.Advancesin Neural Information Processing Systems, 38, 2026.

DiDPO: Diff-in-Diff Policy Optimization for Coding Agent Training Icpc-eval: Probing the frontiers of llm reasoning with competitive pro- gramming contests.Advancesin Neural Information Processing Systems, 38, 2026

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:00:54.363007Z

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 fac97e41-cf4b-4f13-a262-43bc38f2b12b · outbound

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

DiDPO: Diff-in-Diff Policy Optimization for Coding Agent Training Swe-agent: Agent-computer inter- faces enable automated software engineering

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:00:54.335861Z

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 ea1ef2f3-8f9b-46eb-8f3d-373ca0326653 · outbound

This paper cites Webshop: Towards scalable real-world web interaction with grounded language agents.

DiDPO: Diff-in-Diff Policy Optimization for Coding Agent Training Webshop: Towards scalable real-world web interaction with grounded language agents

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:00:54.314717Z

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 cfdc3d24-d520-4968-9b00-34595f0ed208 · outbound

This paper cites React: Syn- ergizing reasoning and acting in language models.

DiDPO: Diff-in-Diff Policy Optimization for Coding Agent Training React: Syn- ergizing reasoning and acting in language models

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:00:54.290530Z

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 50916849-3599-4b50-beee-4b8a7bb28f7a · outbound

This paper cites Dapo: An open- source llm reinforcement learning system at scale.

DiDPO: Diff-in-Diff Policy Optimization for Coding Agent Training Dapo: An open- source llm reinforcement learning system at scale

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:00:54.272093Z

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 1aeb31ca-10f6-442b-b360-d62a0cecf979 · outbound

This paper cites Group Sequence Policy Optimization.

DiDPO: Diff-in-Diff Policy Optimization for Coding Agent Training Group Sequence Policy Optimization

Reference 60

Resolution: unresolved; no resolver link, observed 2026-08-10T14:00:53.210565Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1f4b7440-a570-47d8-b43f-e20b6eeeca5a · outbound

This paper cites A syntax-guided edit decoder for neural pro- gram repair.

DiDPO: Diff-in-Diff Policy Optimization for Coding Agent Training A syntax-guided edit decoder for neural pro- gram repair

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:00:54.249562Z

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 5d6bbe64-8f09-4f69-80c5-bd33ead92af8 · outbound

This paper cites GAGPO: Generalized Advantage Grouped Policy Optimization.

DiDPO: Diff-in-Diff Policy Optimization for Coding Agent Training GAGPO: Generalized Advantage Grouped Policy Optimization

Reference 62

Resolution: unresolved; no resolver link, observed 2026-08-10T14:00:53.226387Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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