Paper Citation Record · LEDGER

Training Agents using Upside-Down Reinforcement Learning

As of 19 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 18 inbound Pith citation observations for arXiv:1912.02877.

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

pith.paper-citation-record.v1
1912.02877 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 18 of 18 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+00:00

measured 18 of 18 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T21:45:57.537078Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-02T21:57:25.915173Z

Reference resolution

0 of 0 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 80bf680b-5b57-4d18-8841-ae1b5076cacc · inbound

Decision Transformer: Reinforcement Learning via Sequence Modeling cites this paper.

Decision Transformer: Reinforcement Learning via Sequence Modeling Training Agents using Upside-Down Reinforcement Learning

Reference 43

Resolution: verified exact; arxiv_id, observed 2026-05-18T15:11:11.117837Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 1baf92be-02ec-4bbb-be46-cfa2500d5d2e · inbound

Is Conditional Generative Modeling all you need for Decision-Making? cites this paper.

Is Conditional Generative Modeling all you need for Decision-Making? Training Agents using Upside-Down Reinforcement Learning

Reference 209

Resolution: metadata mismatch; arxiv_id, observed 2026-05-15T15:35:10.858704Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 38f45699-690a-442d-9483-3fb582ba6590 · inbound

MetaGPT: Meta Programming for A Multi-Agent Collaborative Framework cites this paper.

MetaGPT: Meta Programming for A Multi-Agent Collaborative Framework Training Agents using Upside-Down Reinforcement Learning

Reference 150

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T03:43:19.125664Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 0ff21285-d901-4b47-901b-4138ce934c81 · inbound

Upside-Down Reinforcement Learning for More Interpretable Optimal Control cites this paper.

Upside-Down Reinforcement Learning for More Interpretable Optimal Control Training Agents using Upside-Down Reinforcement Learning

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-12T18:35:59.778062Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9bd1bbdf-79b5-440e-9209-b86158fa4055 · inbound

Heuristically Adaptive Diffusion-Model Evolutionary Strategy cites this paper.

Heuristically Adaptive Diffusion-Model Evolutionary Strategy Training Agents using Upside-Down Reinforcement Learning

Reference 96

Resolution: unresolved; no resolver link, observed 2026-08-12T16:32:34.761175Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 30b68d5f-2cf3-4bf6-9ce7-1411877a6585 · inbound

The broader spectrum of in-context learning cites this paper.

The broader spectrum of in-context learning Training Agents using Upside-Down Reinforcement Learning

Reference 84

Resolution: unresolved; no resolver link, observed 2026-08-11T22:10:22.971826Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bb27b557-e7ef-4f33-b183-0d538c203ee5 · inbound

MGDA: Model-based Goal Data Augmentation for Offline Goal-conditioned Weighted Supervised Learning cites this paper.

MGDA: Model-based Goal Data Augmentation for Offline Goal-conditioned Weighted Supervised Learning Training Agents using Upside-Down Reinforcement Learning

Reference 49

Resolution: unresolved; no resolver link, observed 2026-08-11T15:02:44.262016Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 788f03c6-d461-45cc-a387-7cfd7518c8fc · inbound

Beyond the Known: Decision Making with Counterfactual Reasoning Decision Transformer cites this paper.

Beyond the Known: Decision Making with Counterfactual Reasoning Decision Transformer Training Agents using Upside-Down Reinforcement Learning

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-15T21:45:57.537078Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fc5c38cc-235b-47fc-a44a-eabe153027c2 · inbound

A Provable Approach for End-to-End Safe Reinforcement Learning cites this paper.

A Provable Approach for End-to-End Safe Reinforcement Learning Training Agents using Upside-Down Reinforcement Learning

Reference 46

Resolution: unresolved; no resolver link, observed 2026-08-07T13:30:24.270417Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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

How to Provably Improve Return Conditioned Supervised Learning? cites this paper.

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

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 325a3a7e-8201-4ff1-971f-797eabf7a0e8 · inbound

Behavioral Exploration: Learning to Explore via In-Context Adaptation cites this paper.

Behavioral Exploration: Learning to Explore via In-Context Adaptation Training Agents using Upside-Down Reinforcement Learning

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-06T18:15:46.817188Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 715459ac-bfe6-476e-b793-aea24c7a4984 · inbound

Equivariant Goal Conditioned Contrastive Reinforcement Learning cites this paper.

Equivariant Goal Conditioned Contrastive Reinforcement Learning Training Agents using Upside-Down Reinforcement Learning

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-06T15:26:24.543536Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 28fa6eef-fb21-4a98-897d-7278f3b6ea36 · inbound

GeoExplorer: Active Geo-localization with Curiosity-Driven Exploration cites this paper.

GeoExplorer: Active Geo-localization with Curiosity-Driven Exploration Training Agents using Upside-Down Reinforcement Learning

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-06T10:27:14.448302Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 310d9c01-bbdb-4460-8592-6e9a51c52323 · inbound

Success Conditioning as Policy Improvement: The Optimization Problem Solved by Imitating Success cites this paper.

Success Conditioning as Policy Improvement: The Optimization Problem Solved by Imitating Success Training Agents using Upside-Down Reinforcement Learning

Reference 2017

Resolution: unresolved; no resolver link, observed 2026-08-03T08:14:02.544642Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 07d1701b-aded-4818-9ad6-1aa0f6fc72eb · inbound

QHyer: Q-conditioned Hybrid Attention-mamba Transformer for Offline Goal-conditioned RL cites this paper.

QHyer: Q-conditioned Hybrid Attention-mamba Transformer for Offline Goal-conditioned RL Training Agents using Upside-Down Reinforcement Learning

Reference 232

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T04:30:58.178293Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation b33bb334-208a-4f7b-b6bd-9a6d9280bba5 · inbound

Neuro-Symbolic Injection of LTLf Constraints in Autoregressive Reinforcement Learning Policies cites this paper.

Neuro-Symbolic Injection of LTLf Constraints in Autoregressive Reinforcement Learning Policies Training Agents using Upside-Down Reinforcement Learning

Reference 17

Resolution: verified exact; arxiv_id, observed 2026-07-02T21:57:25.916771Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation fc21b51e-44c7-4011-86bc-1d869c0540fc · inbound

Reinforcement Learning: From Algorithms To Foundation Models cites this paper.

Reinforcement Learning: From Algorithms To Foundation Models Training Agents using Upside-Down Reinforcement Learning

Reference 174

Resolution: unresolved; no resolver link, observed 2026-08-01T17:45:14.105428Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 008ffa89-a4da-4946-90dd-a2c1f29c159c · inbound

LEMUR: Learning to Align with Multi-Objective Reinforcement Learning from Preference Feedback cites this paper.

LEMUR: Learning to Align with Multi-Objective Reinforcement Learning from Preference Feedback Training Agents using Upside-Down Reinforcement Learning

Reference 269

Resolution: unresolved; no resolver link, observed 2026-08-03T04:39:32.197052Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.