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Paper Citation Record · LEDGER

Can Large Language Models Develop Strategic Reasoning? Post-training Insights from Learning Chess

As of 7 August 2026, this Paper Citation Record lists 34 of 34 outbound references and 1 inbound Pith citation observation for arXiv:2507.00726.

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

pith.paper-citation-record.v1
2507.00726 v3

Coverage vector

measured 34 of 34 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T21:13:15.438371Z

measured 35 of 35 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-01T08:57:02.003590Z

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

34 of 34 outbound references displayed

  • verified exact1
  • verified fuzzy5
  • unresolved28
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 4f523d3a-94c7-4424-857c-5d1edba60f18 · outbound

This paper cites write newline.

Can Large Language Models Develop Strategic Reasoning? Post-training Insights from Learning Chess write newline

Reference 1

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T21:13:14.562989Z digest=sha256:151add6fa687a98ad7cb8204ec4c161ce4e233aeb0e41ca3c551b23842f91b46

Observation 45a3217f-1ab5-4a3c-bac4-a3fa983b97bb · outbound

This paper cites an unresolved cited work.

Can Large Language Models Develop Strategic Reasoning? Post-training Insights from Learning Chess Unresolved cited work

Reference 2

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raw_fallback, observed 2026-08-06T21:13:16.011700Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-06T21:13:14.602250Z digest=sha256:6c778508fd26cc1ff0df327fd8d501ca5eb333ca6af59bea3b6cb372bea92216

Observation 50699f88-3097-4635-9f23-0e6dc5c007cf · outbound

This paper cites Chessgpt: Bridging policy learning and language modeling.

Can Large Language Models Develop Strategic Reasoning? Post-training Insights from Learning Chess Chessgpt: Bridging policy learning and language modeling

Reference 3

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raw_fallback, observed 2026-08-06T21:13:15.995114Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-06T21:13:14.636904Z digest=sha256:5d3e158fe970f20528cff97dcd29a4374631ba474e02edc9436cac124171c8d8

Observation 1478d935-a06a-48d6-bd50-aada7116b6cc · outbound

This paper cites The Llama 3 Herd of Models.

Can Large Language Models Develop Strategic Reasoning? Post-training Insights from Learning Chess The Llama 3 Herd of Models

Reference 4

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source=arxiv_source observed=2026-08-06T21:13:14.673504Z digest=sha256:10d54296157a5804022493f552bc35ed0b42e73a0ec63aac1e3d8c4467730474

Observation 5a9e949a-8796-4961-8374-ef58ad103bde · outbound

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

Can Large Language Models Develop Strategic Reasoning? Post-training Insights from Learning Chess DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Reference 5

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source=arxiv_source observed=2026-08-06T21:13:14.708823Z digest=sha256:37196b3558d402b0970efaf82f02717428d4959ee73937d4c7e67de854dc904c

Observation a27acc06-c949-4c8c-8792-6b2e0fbe7e01 · outbound

This paper cites Learning to Reason for Long-Form Story Generation.

Can Large Language Models Develop Strategic Reasoning? Post-training Insights from Learning Chess Learning to Reason for Long-Form Story Generation

Reference 6

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T21:13:14.743741Z digest=sha256:915f0da85d5b78be0ecab38b4d12c5705901ddaec92c94c399e9572c29468b1d

Observation 219e7b23-2f66-41dd-a309-695a3ec33382 · outbound

This paper cites Improving regression performance with distributional losses.

Can Large Language Models Develop Strategic Reasoning? Post-training Insights from Learning Chess Improving regression performance with distributional losses

Reference 7

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verified fuzzy
raw_fallback, observed 2026-08-06T21:13:15.977225Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-06T21:13:14.778740Z digest=sha256:167f3aa557880e779b16043ae433e55613a5e2cabf96ed3a9746266b3ac702f7

Observation 67fda46f-0282-43ae-ab8e-130dc8072787 · outbound

This paper cites Bridging the gap between expert and language models: Concept-guided chess commentary generation and evaluation.

Can Large Language Models Develop Strategic Reasoning? Post-training Insights from Learning Chess Bridging the gap between expert and language models: Concept-guided chess commentary generation and evaluation

Reference 8

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raw_fallback, observed 2026-08-06T21:13:15.958182Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-06T21:13:14.814753Z digest=sha256:7e8098ec53ff399805c4c5e9ff94e0cdb2d205e52dbee8ba1f8887d2c94a7ba0

Observation 37d0b883-01d8-4c91-8fb0-10d8a25cfbaa · outbound

This paper cites Tulu 3: Pushing Frontiers in Open Language Model Post-Training.

Can Large Language Models Develop Strategic Reasoning? Post-training Insights from Learning Chess Tulu 3: Pushing Frontiers in Open Language Model Post-Training

Reference 9

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source=arxiv_source observed=2026-08-06T21:13:14.849487Z digest=sha256:301fcbf8a1bfd983a9ebf810054c5598b0c7d5844f7d71c2cb55db37bef1b3e1

Observation 12671650-3fc0-4ac1-a651-65143299ec16 · outbound

This paper cites LLMs Can Easily Learn to Reason from Demonstrations Structure, not content, is what matters!.

Can Large Language Models Develop Strategic Reasoning? Post-training Insights from Learning Chess LLMs Can Easily Learn to Reason from Demonstrations Structure, not content, is what matters!

Reference 10

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source=arxiv_source observed=2026-08-06T21:13:14.885294Z digest=sha256:74c41a6166719feff816a80f2f745d35e01c20878677729fd37241b3ec3ff7c6

Observation 7265002d-5809-4e71-8fea-62d52dd94b3e · outbound

This paper cites Understanding R1-Zero-Like Training: A Critical Perspective.

Can Large Language Models Develop Strategic Reasoning? Post-training Insights from Learning Chess Understanding R1-Zero-Like Training: A Critical Perspective

Reference 11

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source=arxiv_source observed=2026-08-06T21:13:14.920254Z digest=sha256:e31b9bb62578e8654c2a9bff13bb23d7329f49c8e6b060145280aa5b23758f96

Observation cf7ab677-32a9-444a-bad0-724cf9baecde · outbound

This paper cites Visual-RFT: Visual Reinforcement Fine-Tuning.

Can Large Language Models Develop Strategic Reasoning? Post-training Insights from Learning Chess Visual-RFT: Visual Reinforcement Fine-Tuning

Reference 12

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source=arxiv_source observed=2026-08-06T21:13:14.955840Z digest=sha256:4c083b8e4c98dc51c4ef103fc1873d4d0143abf15d8825bb2bbcee5c5390fb32

Observation e6de8796-08e3-4c08-83e8-a16d9920ca50 · outbound

This paper cites Orak: A Foundational Benchmark for Training and Evaluating LLM Agents on Diverse Video Games.

Can Large Language Models Develop Strategic Reasoning? Post-training Insights from Learning Chess Orak: A Foundational Benchmark for Training and Evaluating LLM Agents on Diverse Video Games

Reference 13

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source=arxiv_source observed=2026-08-06T21:13:14.990922Z digest=sha256:d419767625ee3abbaeb9e1a051de198a3e8dedcf249399525c67faa1eaa933df

Observation c5723c9a-921d-4b84-8298-04b430d1952d · outbound

This paper cites Qwen2.5 Technical Report.

Can Large Language Models Develop Strategic Reasoning? Post-training Insights from Learning Chess Qwen2.5 Technical Report

Reference 14

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source=arxiv_source observed=2026-08-06T21:13:15.027217Z digest=sha256:cce8f3f216a82a3e14822e20ed045df2684f0650c51b03d799eb1a7df6af79f8

Observation 720c2cf2-b5e0-4e70-ab0c-5b5526cd2638 · outbound

This paper cites Amortized planning with large-scale transformers: A case study on chess.

Can Large Language Models Develop Strategic Reasoning? Post-training Insights from Learning Chess Amortized planning with large-scale transformers: A case study on chess

Reference 15

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raw_fallback, observed 2026-08-06T21:13:15.941797Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-06T21:13:15.062066Z digest=sha256:6c677c1358ab8e318289cc561273c239ab8c5ac24e1fe26a9f5802de2b84c8a6

Observation bf7edc7d-f230-4347-9a5c-06a11217689c · outbound

This paper cites Spurious Rewards: Rethinking Training Signals in RLVR.

Can Large Language Models Develop Strategic Reasoning? Post-training Insights from Learning Chess Spurious Rewards: Rethinking Training Signals in RLVR

Reference 16

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source=arxiv_source observed=2026-08-06T21:13:15.096866Z digest=sha256:12a65c9bf6fbc15bff150ea255a7197729d5e2fb238e25632addebb3a3aa45f3

Observation f464150f-8539-46dd-9e52-8aaf5f9616dc · outbound

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

Can Large Language Models Develop Strategic Reasoning? Post-training Insights from Learning Chess DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models

Reference 17

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source=arxiv_source observed=2026-08-06T21:13:15.133788Z digest=sha256:60994283b0d0ba19d435dd7266b6b1c143c7d2a28f1b316085b2eaf4c370c3c9

Observation 9dd87893-dd80-4af9-8fa7-5460330dd696 · outbound

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

Can Large Language Models Develop Strategic Reasoning? Post-training Insights from Learning Chess HybridFlow: A Flexible and Efficient RLHF Framework

Reference 18

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source=arxiv_source observed=2026-08-06T21:13:15.168117Z digest=sha256:8bd62eb55971106e38966cbb7de157174dfd702275b1de725f08d49a0ce134db

Observation 9e9e4522-fbce-4ae5-ac6d-afa829209472 · outbound

This paper cites Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm.

Can Large Language Models Develop Strategic Reasoning? Post-training Insights from Learning Chess Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm

Reference 19

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source=arxiv_source observed=2026-08-06T21:13:15.204837Z digest=sha256:260255c07fb2bde4a2b6bd52a81ab62daca12f22e8b41206b27a4d22e9865031

Observation ac25e364-83af-4805-bc3c-61e9b5e52045 · outbound

This paper cites Attention is all you need.

Can Large Language Models Develop Strategic Reasoning? Post-training Insights from Learning Chess Attention is all you need

Reference 20

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source=arxiv_source observed=2026-08-06T21:13:15.240659Z digest=sha256:aa7cb6d009ea1735f52631f7da28399ad7e16845f7ff7030cc756823f8981eb7

Observation 32e410e0-aecd-43f8-b37e-3f295d39d486 · outbound

This paper cites Explore the Reasoning Capability of LLMs in the Chess Testbed.

Can Large Language Models Develop Strategic Reasoning? Post-training Insights from Learning Chess Explore the Reasoning Capability of LLMs in the Chess Testbed

Reference 21

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local_arxiv, observed 2026-08-06T21:13:15.641036Z

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No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-06T21:13:15.275175Z digest=sha256:89bd51bb1457bb883c3e09147b1b0fd18b528a3c19af8873322cd2144b02d91b

Observation d94fe74a-0bdd-4957-91ea-b8875bf22864 · outbound

This paper cites Reinforcement Learning for Reasoning in Large Language Models with One Training Example.

Can Large Language Models Develop Strategic Reasoning? Post-training Insights from Learning Chess Reinforcement Learning for Reasoning in Large Language Models with One Training Example

Reference 22

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source=arxiv_source observed=2026-08-06T21:13:15.311542Z digest=sha256:6b44ad8746d93417112efbcea2a0cc59e93e25475871b659eb363331806e1d62

Observation 1e8cd449-4306-4088-8306-33b72a2f453f · outbound

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

Can Large Language Models Develop Strategic Reasoning? Post-training Insights from Learning Chess DAPO: An Open-Source LLM Reinforcement Learning System at Scale

Reference 23

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source=arxiv_source observed=2026-08-06T21:13:15.347231Z digest=sha256:ca4b39225a3e68ac96b36feae4468f88c4eac8b867f49c7358bd43d84267ba49

Observation a3dbd584-e7dc-47d8-80f3-c14f00d2d54f · outbound

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

Can Large Language Models Develop Strategic Reasoning? Post-training Insights from Learning Chess Does Reinforcement Learning Really Incentivize Reasoning Capacity in LLMs Beyond the Base Model?

Reference 24

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source=arxiv_source observed=2026-08-06T21:13:15.383047Z digest=sha256:cd166bf0f0fdc6c2b29a6d701697dbce16c0124888e5538bf4444eee4d1fb39a

Observation ffb13532-71c1-4418-b679-f952e7921f18 · outbound

This paper cites Med-RLVR: Emerging Medical Reasoning from a 3B base model via reinforcement Learning.

Can Large Language Models Develop Strategic Reasoning? Post-training Insights from Learning Chess Med-RLVR: Emerging Medical Reasoning from a 3B base model via reinforcement Learning

Reference 25

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source=arxiv_source observed=2026-08-06T21:13:15.388077Z digest=sha256:7e00e93e39a067d31c7165bbd72483e5200e874853707aa4f0712452b7960db2

Observation 30d2e018-9101-4a3e-b509-31e9071a9cfa · outbound

This paper cites LLM as a Mastermind: A Survey of Strategic Reasoning with Large Language Models.

Can Large Language Models Develop Strategic Reasoning? Post-training Insights from Learning Chess LLM as a Mastermind: A Survey of Strategic Reasoning with Large Language Models

Reference 26

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source=arxiv_source observed=2026-08-06T21:13:15.393896Z digest=sha256:2862cd2fda7050cf62fe2e015c551a60f91a3cf382897e2b109317399749804d

Observation 27f828f5-7772-42e0-878a-8a4b404ad8a7 · outbound

This paper cites Complete chess games enable LLM become a chess master.

Can Large Language Models Develop Strategic Reasoning? Post-training Insights from Learning Chess Complete chess games enable LLM become a chess master

Reference 27

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raw_fallback, observed 2026-08-06T21:13:15.912353Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-06T21:13:15.399974Z digest=sha256:dbb098386a9aac91448996300089a5878e72905cecd119f9f5ed01b6bc7a3e08

Observation 2e6717eb-45a6-40a0-ab59-0e9be9669c13 · outbound

This paper cites Distill Not Only Data but Also Rewards: Can Smaller Language Models Surpass Larger Ones?.

Can Large Language Models Develop Strategic Reasoning? Post-training Insights from Learning Chess Distill Not Only Data but Also Rewards: Can Smaller Language Models Surpass Larger Ones?

Reference 28

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source=arxiv_source observed=2026-08-06T21:13:15.405997Z digest=sha256:577850b980f684872db724d6941b3ddac74e958b915b8235a80f68071ba8660c

Observation 3d424eeb-176a-41e2-9fe5-0ff6b21aa5d9 · outbound

This paper cites Absolute Zero: Reinforced Self-play Reasoning with Zero Data.

Can Large Language Models Develop Strategic Reasoning? Post-training Insights from Learning Chess Absolute Zero: Reinforced Self-play Reasoning with Zero Data

Reference 29

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no resolver link, observed 2026-08-06T21:13:15.411620Z

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source=arxiv_source observed=2026-08-06T21:13:15.411620Z digest=sha256:82dcde968f73e6d42f29004328bcb58513527ad8c8cb09327ae1b593f84d31cd

Observation cbb9ee45-ebdf-46fe-9dd0-c32d2aec739b · outbound

This paper cites Echo Chamber: RL Post-training Amplifies Behaviors Learned in Pretraining.

Can Large Language Models Develop Strategic Reasoning? Post-training Insights from Learning Chess Echo Chamber: RL Post-training Amplifies Behaviors Learned in Pretraining

Reference 30

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source=arxiv_source observed=2026-08-06T21:13:15.416519Z digest=sha256:1e0554a2e8959e21bfc137f7ccd21f434281da4afda862fd869020a4fc46c375

Observation 325fed64-2de9-4f52-a07f-19e0f6dd0cb4 · outbound

This paper cites LlamaFactory: Unified Efficient Fine-Tuning of 100+ Language Models.

Can Large Language Models Develop Strategic Reasoning? Post-training Insights from Learning Chess LlamaFactory: Unified Efficient Fine-Tuning of 100+ Language Models

Reference 31

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source=arxiv_source observed=2026-08-06T21:13:15.421675Z digest=sha256:f083001ea7c43648ca3d98ab73a7b9cb862f2836f9d7a7fdc52a2cf055209eeb

Observation 5f8ed20e-7cc6-4427-a66c-026994472dbf · outbound

This paper cites @esa (Ref.

Can Large Language Models Develop Strategic Reasoning? Post-training Insights from Learning Chess @esa (Ref

Reference 32

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source=arxiv_source observed=2026-08-06T21:13:15.427861Z digest=sha256:a5bbf718cfe6635495a9ec7106ec8572f16df6f596cdcf73193101bea9f68731

Observation fbae588f-ffbb-4ce6-8647-1327ecf9de9e · outbound

This paper cites an unresolved cited work.

Can Large Language Models Develop Strategic Reasoning? Post-training Insights from Learning Chess Unresolved cited work

Reference 33

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source=arxiv_source observed=2026-08-06T21:13:15.433040Z digest=sha256:39c51fbf5f90446da4493a55b5347325eff266b1f1508b7786d0af85c996e014

Observation a4c06d96-8721-4c7a-93a9-4a33430379b9 · outbound

This paper cites an unresolved cited work.

Can Large Language Models Develop Strategic Reasoning? Post-training Insights from Learning Chess Unresolved cited work

Reference 34

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source=arxiv_source observed=2026-08-06T21:13:15.438371Z digest=sha256:3302b422b9d02ede09026dafd9be53d157ccb3376de776bb9506f00a2c675524

Pith citing papers

Observation d7f9eae7-12e7-4379-aaea-ec90088c20bb · inbound

The Weight of Silence: A Causal Case for Weights Over the Scratchpad in Latent Chess Reasoning cites this paper.

The Weight of Silence: A Causal Case for Weights Over the Scratchpad in Latent Chess Reasoning Can Large Language Models Develop Strategic Reasoning? Post-training Insights from Learning Chess

Reference 6

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source=pdf_text observed=2026-08-01T08:57:02.003590Z digest=sha256:855a1ba39b8679b04a9645bb4e116539a21000e405655455a77cade5bd220eb8