Paper Citation Record · LEDGER

How to Provably Improve Return Conditioned Supervised Learning?

As of 11 August 2026, this Paper Citation Record lists 43 of 43 outbound references and 0 inbound Pith citation observations for arXiv:2506.08463.

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

pith.paper-citation-record.v1
2506.08463 v1

Coverage vector

measured 43 of 43 reference resolution

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Source: paper_references, paper_reference_links, observed 2026-08-07T05:19:10.275156Z

measured 43 of 43 standing notices

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Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

43 of 43 outbound references displayed

verified exact0
verified fuzzy34
unresolved8
parse uncertain1
malformed identifier0
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External citation measurements

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Outbound references

Observation fcfc7757-4214-4f91-b109-8f106280f373 · outbound

This paper cites When does return-conditioned supervised learning work for offline reinforcement learning? Advances in Neural Information Processing Systems, 35:1542–1553, 2022.

How to Provably Improve Return Conditioned Supervised Learning? When does return-conditioned supervised learning work for offline reinforcement learning? Advances in Neural Information Processing Systems, 35:1542–1553, 2022

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:19:19.174686Z

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No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 5b9ca6b3-c313-4672-b65b-880e92d71eaa · outbound

This paper cites Decision transformer: Reinforcement learning 1For two distributionsPandQ,P≪QmeansPis absolutely continuous w.r.t.

How to Provably Improve Return Conditioned Supervised Learning? Decision transformer: Reinforcement learning 1For two distributionsPandQ,P≪QmeansPis absolutely continuous w.r.t

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:19:18.989128Z

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No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 632f3e8c-a2d3-4cd7-99cb-80ca75d930d9 · outbound

This paper cites Rvs: What is essential for offline rl via supervised learning? InInternational Conference on Learning Representations,.

How to Provably Improve Return Conditioned Supervised Learning? Rvs: What is essential for offline rl via supervised learning? InInternational Conference on Learning Representations,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:19:18.778572Z

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Observation 8059f672-d218-4f2a-bb9e-0686a58617fe · outbound

This paper cites Imitating past successes can be very suboptimal.Advances in Neural Information Processing Systems, 35:6047–6059, 2022.

How to Provably Improve Return Conditioned Supervised Learning? Imitating past successes can be very suboptimal.Advances in Neural Information Processing Systems, 35:6047–6059, 2022

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:19:18.350689Z

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No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 2ecf1c2e-1149-4524-a53e-55a7f1a8a4b7 · outbound

This paper cites D4RL: Datasets for Deep Data-Driven Reinforcement Learning.

How to Provably Improve Return Conditioned Supervised Learning? D4RL: Datasets for Deep Data-Driven Reinforcement Learning

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-07T05:19:04.868201Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6cbb6611-c082-4463-9026-07bfd459cf24 · outbound

This paper cites A minimalist approach to offline reinforcement learning.

How to Provably Improve Return Conditioned Supervised Learning? A minimalist approach to offline reinforcement learning

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:19:18.154249Z

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No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 5655c842-202c-4de9-a6f0-f83ed89e05dd · outbound

This paper cites Generalized decision transformer for of- fline hindsight information matching.

How to Provably Improve Return Conditioned Supervised Learning? Generalized decision transformer for of- fline hindsight information matching

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:19:17.981506Z

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No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation e2aa48c5-3aa6-4a9d-95c2-bd9a5f79d2c2 · outbound

This paper cites Act: empowering decision transformer with dynamic programming via advantage conditioning.

How to Provably Improve Return Conditioned Supervised Learning? Act: empowering decision transformer with dynamic programming via advantage conditioning

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:19:17.798842Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 3269d05b-354c-48d7-afa8-da6aaa6f9c35 · outbound

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

How to Provably Improve Return Conditioned Supervised Learning? DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-07T05:19:05.443153Z

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Unavailable: canonical work link unavailable.

Observation c368ab03-1013-4091-9e99-89f1c5170047 · outbound

This paper cites Q-value regularized transformer for offline reinforcement learning.

How to Provably Improve Return Conditioned Supervised Learning? Q-value regularized transformer for offline reinforcement learning

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:19:17.590710Z

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Observation 01b99dd9-f6c6-4d7e-8696-ab0812b89bc3 · outbound

This paper cites Offline reinforcement learning as one big sequence modeling problem.Advances in neural information processing systems, 34:1273– 1286, 2021.

How to Provably Improve Return Conditioned Supervised Learning? Offline reinforcement learning as one big sequence modeling problem.Advances in neural information processing systems, 34:1273– 1286, 2021

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:19:17.389293Z

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Observation a250da26-9d7a-4fe9-8485-5e895f9eb332 · outbound

This paper cites Is pessimism provably efficient for offline rl? In International Conference on Machine Learning, pages 5084–5096.

How to Provably Improve Return Conditioned Supervised Learning? Is pessimism provably efficient for offline rl? In International Conference on Machine Learning, pages 5084–5096

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:19:17.179796Z

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No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 7f6df767-4bcd-4739-8a4c-fab9b5d99d93 · outbound

This paper cites Reinforcement learning in robotics: A survey.

How to Provably Improve Return Conditioned Supervised Learning? Reinforcement learning in robotics: A survey

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:19:16.979615Z

Source-reported events for the cited work

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Observation cd59261a-7881-4ac4-bc9b-a754d3a0f442 · outbound

This paper cites Quantile regression.Journal of economic perspectives, 15(4):143–156, 2001.

How to Provably Improve Return Conditioned Supervised Learning? Quantile regression.Journal of economic perspectives, 15(4):143–156, 2001

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:19:16.814473Z

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Observation f822402f-8bc0-4912-83f8-edbe38a56fe4 · outbound

This paper cites Offline reinforcement learning with implicit q-learning.

How to Provably Improve Return Conditioned Supervised Learning? Offline reinforcement learning with implicit q-learning

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:19:16.614230Z

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Observation 6c5875f2-64b1-4600-ae59-09958a242422 · outbound

This paper cites Reward-Conditioned Policies.

How to Provably Improve Return Conditioned Supervised Learning? Reward-Conditioned Policies

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-07T05:19:06.527500Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b8b33b93-0360-41c1-ae74-3d9cf32ad1c5 · outbound

This paper cites Conservative q-learning for offline reinforcement learning.Advances in Neural Information Processing Systems, 33: 1179–1191, 2020.

How to Provably Improve Return Conditioned Supervised Learning? Conservative q-learning for offline reinforcement learning.Advances in Neural Information Processing Systems, 33: 1179–1191, 2020

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:19:16.417121Z

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Observation 767bf042-42de-4d6b-abd8-08006375deda · outbound

This paper cites Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problems.

How to Provably Improve Return Conditioned Supervised Learning? Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problems

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-07T05:19:06.860433Z

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Unavailable: canonical work link unavailable.

Observation 2fe8ce3c-613d-4c92-9751-86e2c1fe269f · outbound

This paper cites Deep rein- forcement learning for dynamic treatment regimes on medical registry data.

How to Provably Improve Return Conditioned Supervised Learning? Deep rein- forcement learning for dynamic treatment regimes on medical registry data

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:19:16.248503Z

Source-reported events for the cited work

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Observation 8858455d-b390-4bb0-acae-6186137f7915 · outbound

This paper cites Deep spatial q- learning for infectious disease control.Journal of Agricultural, Biological and Environmental Statistics, 28(4):749–773, 2023.

How to Provably Improve Return Conditioned Supervised Learning? Deep spatial q- learning for infectious disease control.Journal of Agricultural, Biological and Environmental Statistics, 28(4):749–773, 2023

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:19:16.058975Z

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No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 9b0d61b0-f99f-49c0-a347-8858d116fba9 · outbound

This paper cites Human-level control through deep reinforcement learning.nature, 518(7540):529–533, 2015.

How to Provably Improve Return Conditioned Supervised Learning? Human-level control through deep reinforcement learning.nature, 518(7540):529–533, 2015

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:19:15.881289Z

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Observation be7d3c4b-a6ba-4257-915f-69937ccbad93 · outbound

This paper cites Asymmetric least squares estimation and testing.

How to Provably Improve Return Conditioned Supervised Learning? Asymmetric least squares estimation and testing

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:19:13.905451Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 90b11ce6-50d2-4a37-b5d2-e924eca9d161 · outbound

This paper cites You can’t count on luck: Why decision transformers and rvs fail in stochastic environments.Advances in neural information processing systems, 35:38966–38979, 2022.

How to Provably Improve Return Conditioned Supervised Learning? You can’t count on luck: Why decision transformers and rvs fail in stochastic environments.Advances in neural information processing systems, 35:38966–38979, 2022

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:19:13.732547Z

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Observation 5717991e-91ec-4bfa-ba3a-df3a467f88cf · outbound

This paper cites Reinforcement Learning Upside Down: Don't Predict Rewards -- Just Map Them to Actions.

How to Provably Improve Return Conditioned Supervised Learning? Reinforcement Learning Upside Down: Don't Predict Rewards -- Just Map Them to Actions

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-07T05:19:07.756599Z

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Unavailable: canonical work link unavailable.

Observation 1cca5d79-1339-4d7a-a4c9-664a40f50954 · outbound

This paper cites Reinforcement learning in robotic applications: a comprehensive survey.Artificial Intelligence Review, 55(2):945–990, 2022.

How to Provably Improve Return Conditioned Supervised Learning? Reinforcement learning in robotic applications: a comprehensive survey.Artificial Intelligence Review, 55(2):945–990, 2022

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:19:13.536857Z

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Observation 1e69c1d7-7926-44ab-b3ac-bb44ef679d6c · outbound

This paper cites Training Agents using Upside-Down Reinforcement Learning.

How to Provably Improve Return Conditioned Supervised Learning? Training Agents using Upside-Down Reinforcement Learning

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-07T05:19:08.014844Z

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Observation 2afd2b9d-7214-4a41-9df1-ea4084ea1283 · outbound

This paper cites MIT press,.

How to Provably Improve Return Conditioned Supervised Learning? MIT press,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:19:13.380306Z

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No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 721c7b5e-3a48-45fe-9077-1e0d3ef24a7a · outbound

This paper cites Temporal difference learning and td-gammon.Communications of the ACM, 38(3):58–68, 1995.

How to Provably Improve Return Conditioned Supervised Learning? Temporal difference learning and td-gammon.Communications of the ACM, 38(3):58–68, 1995

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:19:13.180697Z

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No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 87aa220a-a2ed-477e-a513-63bba5759d4e · outbound

This paper cites Grandmaster level in starcraft ii using multi-agent reinforcement learning.nature, 575(7782):350–354, 2019.

How to Provably Improve Return Conditioned Supervised Learning? Grandmaster level in starcraft ii using multi-agent reinforcement learning.nature, 575(7782):350–354, 2019

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:19:13.005163Z

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Observation 6141da23-8251-45f2-a0d1-7b5dbb5fea7e · outbound

This paper cites Return augmented decision transformer for off-dynamics reinforcement learning.arXiv preprint arXiv:2410.23450, 2024.

How to Provably Improve Return Conditioned Supervised Learning? Return augmented decision transformer for off-dynamics reinforcement learning.arXiv preprint arXiv:2410.23450, 2024

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-07T05:19:08.615517Z

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Observation ff035164-e227-4e2b-a65f-8cbc31daef0d · outbound

This paper cites Q-learning.Machine learning, 8:279–292, 1992.

How to Provably Improve Return Conditioned Supervised Learning? Q-learning.Machine learning, 8:279–292, 1992

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:19:12.809949Z

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Observation 53389b36-65f8-4b59-9e7f-1c542c47ee22 · outbound

This paper cites Elastic decision transformer.Advances in Neural Information Processing Systems, 36, 2024.

How to Provably Improve Return Conditioned Supervised Learning? Elastic decision transformer.Advances in Neural Information Processing Systems, 36, 2024

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:19:12.608655Z

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Observation c631914c-ae8a-4a8d-9b28-261d8d59acbb · outbound

This paper cites A policy-guided imitation approach for offline reinforcement learning.Advances in neural information processing systems, 35: 4085–4098, 2022.

How to Provably Improve Return Conditioned Supervised Learning? A policy-guided imitation approach for offline reinforcement learning.Advances in neural information processing systems, 35: 4085–4098, 2022

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:19:12.409417Z

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No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation f7854296-a476-4262-841e-cfb35771bd49 · outbound

This paper cites Q-learning decision transformer: Leveraging dynamic programming for conditional sequence modelling in offline rl.

How to Provably Improve Return Conditioned Supervised Learning? Q-learning decision transformer: Leveraging dynamic programming for conditional sequence modelling in offline rl

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:19:12.219928Z

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Observation 43b19276-ba88-4e84-8dcb-7e19465c5716 · outbound

This paper cites Dichotomy of control: Sepa- rating what you can control from what you cannot.

How to Provably Improve Return Conditioned Supervised Learning? Dichotomy of control: Sepa- rating what you can control from what you cannot

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:19:12.062449Z

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Observation a50769dc-a148-47b9-9a8a-774bd96bf1c9 · outbound

This paper cites Reinforcement learning in healthcare: A survey.ACM Computing Surveys (CSUR), 55(1):1–36, 2021.

How to Provably Improve Return Conditioned Supervised Learning? Reinforcement learning in healthcare: A survey.ACM Computing Surveys (CSUR), 55(1):1–36, 2021

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:19:11.895118Z

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Observation 0b35ec78-9d8d-40d5-bf81-1186ecea1ed2 · outbound

This paper cites Online decision transformer.

How to Provably Improve Return Conditioned Supervised Learning? Online decision transformer

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:19:11.717619Z

Source-reported events for the cited work

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Observation 544670a7-2e87-4095-8ce6-248819a8584e · outbound

This paper cites How does goal relabeling improve sample efficiency? InProceedings of the 41st International Conference on Machine Learning, pages 61246–61266.

How to Provably Improve Return Conditioned Supervised Learning? How does goal relabeling improve sample efficiency? InProceedings of the 41st International Conference on Machine Learning, pages 61246–61266

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:19:11.514778Z

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Observation 77d681dd-db1b-4cca-95f7-8e794631c989 · outbound

This paper cites an unresolved cited work.

How to Provably Improve Return Conditioned Supervised Learning? Unresolved cited work

Reference 39

Resolution: unresolved; raw_fallback, observed 2026-08-07T05:19:11.309789Z

Source-reported events for the cited work

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Observation 496aea22-1320-433e-a1cb-28b8a0d6fb68 · outbound

This paper cites Reinformer: Max- return sequence modeling for offline rl.

How to Provably Improve Return Conditioned Supervised Learning? Reinformer: Max- return sequence modeling for offline rl

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:19:11.115470Z

Source-reported events for the cited work

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Observation 55a97671-7fdc-4e24-9518-930d3c5a772d · outbound

This paper cites Starting from the second stage, π⋆ starts stitching the performance of different trajectories.

How to Provably Improve Return Conditioned Supervised Learning? Starting from the second stage, π⋆ starts stitching the performance of different trajectories

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:19:10.911021Z

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Observation cd6d66ec-ab1a-41b8-a2fb-2b6f7c8dac22 · outbound

This paper cites We formulate the loss function as LDT−R 2CSL =E τ [−logπ θ(·|τ)−λH(π θ(·|τ))].

How to Provably Improve Return Conditioned Supervised Learning? We formulate the loss function as LDT−R 2CSL =E τ [−logπ θ(·|τ)−λH(π θ(·|τ))]

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:19:10.716042Z

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Observation da474c82-2cbc-41c7-847d-50e88d987a07 · outbound

This paper cites an unresolved cited work.

How to Provably Improve Return Conditioned Supervised Learning? Unresolved cited work

Reference 2022

Resolution: parse uncertain; raw_fallback, observed 2026-08-07T05:19:18.566541Z

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Pith citing papers

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