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Paper Citation Record · LEDGER

Learning to Reach Goals via Iterated Supervised Learning

As of 16 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 27 inbound Pith citation observations for arXiv:1912.06088.

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

pith.paper-citation-record.v1
1912.06088 v4

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 27 of 27 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 27 of 27 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T16:27:33.292977Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T06:49:37.764826Z

Reference resolution

0 of 0 outbound references displayed

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External citation measurements

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Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation bd565aa9-bc56-47dc-acfe-3f509a04729c · inbound

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

Decision Transformer: Reinforcement Learning via Sequence Modeling Learning to Reach Goals via Iterated Supervised Learning

Reference 47

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arxiv_id, observed 2026-05-18T15:11:11.146419Z

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.

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Observation 68123a56-28d6-4845-ac1b-34dd7abfb872 · inbound

Should We Learn Contact-Rich Manipulation Policies from Sampling-Based Planners? cites this paper.

Should We Learn Contact-Rich Manipulation Policies from Sampling-Based Planners? Learning to Reach Goals via Iterated Supervised Learning

Reference 31

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source=pdf_text observed=2026-08-11T16:49:59.173266Z digest=sha256:2f2e5fd60245411e994f89da9e0c2fb2d01652f5b6d4351df64b464dec6dfb12

Observation 4db30e35-46e4-42e7-93ed-be61638f6b07 · inbound

Hindsight Planner: A Closed-Loop Few-Shot Planner for Embodied Instruction Following cites this paper.

Hindsight Planner: A Closed-Loop Few-Shot Planner for Embodied Instruction Following Learning to Reach Goals via Iterated Supervised Learning

Reference 14

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Observation a2365892-1fd6-4786-9f4b-ca7f9256155e · inbound

Prompting Decision Transformers for Zero-Shot Reach-Avoid Policies cites this paper.

Prompting Decision Transformers for Zero-Shot Reach-Avoid Policies Learning to Reach Goals via Iterated Supervised Learning

Reference 12

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source=pdf_text observed=2026-08-07T14:21:25.585866Z digest=sha256:47187906cce8dcbf755c9b690c5a21d005a3de7ff6cc7cfef993f4b55a2ded4f

Observation 501446ab-2c25-4b60-8f7b-9466e8128aff · inbound

Normalizing Flows are Capable Models for Continuous Control cites this paper.

Normalizing Flows are Capable Models for Continuous Control Learning to Reach Goals via Iterated Supervised Learning

Reference 29

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no resolver link, observed 2026-08-07T12:49:57.280493Z

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Unavailable: canonical work link unavailable.

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Observation 77335ca6-e087-4292-8657-b2892f585840 · inbound

Efficient Skill Discovery via Regret-Aware Optimization cites this paper.

Efficient Skill Discovery via Regret-Aware Optimization Learning to Reach Goals via Iterated Supervised Learning

Reference 21

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no resolver link, observed 2026-08-06T22:41:34.963486Z

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T22:41:34.963486Z digest=sha256:f5bf0c7c2dcae166757350e015fb417619268473ad8239c6564fb74541479feb

Observation 5cf61d41-82c4-49f2-a1c1-391bcedd557c · inbound

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

Behavioral Exploration: Learning to Explore via In-Context Adaptation Learning to Reach Goals via Iterated Supervised Learning

Reference 21

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Observation 43cca234-29f1-4921-8ad4-e0236c4c209a · inbound

Equivariant Goal Conditioned Contrastive Reinforcement Learning cites this paper.

Equivariant Goal Conditioned Contrastive Reinforcement Learning Learning to Reach Goals via Iterated Supervised Learning

Reference 17

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no resolver link, observed 2026-08-06T15:26:24.538792Z

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Observation 6ad19f8a-a269-4666-9acf-cc9855668f46 · inbound

Generative Sequential Notification Optimization via Multi-Objective Decision Transformers cites this paper.

Generative Sequential Notification Optimization via Multi-Objective Decision Transformers Learning to Reach Goals via Iterated Supervised Learning

Reference 13

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no resolver link, observed 2026-08-05T11:39:57.847334Z

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Unavailable: canonical work link unavailable.

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Observation 9577541d-712f-4db9-ae4d-f9326ebcec4f · inbound

Reinforcement Learning with Anticipation: A Hierarchical Approach for Long-Horizon Tasks cites this paper.

Reinforcement Learning with Anticipation: A Hierarchical Approach for Long-Horizon Tasks Learning to Reach Goals via Iterated Supervised Learning

Reference 12

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no resolver link, observed 2026-08-15T16:27:33.292977Z

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Unavailable: canonical work link unavailable.

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Observation f91be620-0707-400c-ad6c-ff4412ea2e29 · inbound

Compositional Diffusion with Guided Search for Long-Horizon Planning cites this paper.

Compositional Diffusion with Guided Search for Long-Horizon Planning Learning to Reach Goals via Iterated Supervised Learning

Reference 17

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no resolver link, observed 2026-08-03T13:13:43.891012Z

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T13:13:43.891012Z digest=sha256:ef97f7af5a38e2cf0c7d805aef5c42fd00a7de2cff38cd1de68ebe4879dfe686

Observation 61541cb6-5ace-4b62-b49a-b960b8966cbe · inbound

LeWorldModel: Stable End-to-End Joint-Embedding Predictive Architecture from Pixels cites this paper.

LeWorldModel: Stable End-to-End Joint-Embedding Predictive Architecture from Pixels Learning to Reach Goals via Iterated Supervised Learning

Reference 50

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verified exact
arxiv_id, observed 2026-05-15T04:09:22.537167Z

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.

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Observation 6bf7c55e-445b-40e5-b3dd-c16b65b7fb51 · inbound

Efficient Hierarchical Implicit Flow Q-learning for Offline Goal-conditioned Reinforcement Learning cites this paper.

Efficient Hierarchical Implicit Flow Q-learning for Offline Goal-conditioned Reinforcement Learning Learning to Reach Goals via Iterated Supervised Learning

Reference 18

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arxiv_id, observed 2026-05-11T05:20:58.412122Z

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.

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Observation 5aa919d6-8f34-4df2-b8f5-833489def7b7 · inbound

From Answers to Arguments: Toward Trustworthy Clinical Diagnostic Reasoning with Toulmin-Guided Curriculum Goal-Conditioned Learning cites this paper.

From Answers to Arguments: Toward Trustworthy Clinical Diagnostic Reasoning with Toulmin-Guided Curriculum Goal-Conditioned Learning Learning to Reach Goals via Iterated Supervised Learning

Reference 1

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arxiv_id, observed 2026-05-11T08:45:59.479633Z

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.

source=pdf_text observed=2026-05-10T16:30:34.434984Z digest=sha256:7d202c3a905b0f7367a599c3852a67791e334c49a872f1da7fee2af336fafd34

Observation ebc466fa-a539-42e0-8872-1dece75ebddf · inbound

GCImOpt: Learning efficient goal-conditioned policies by imitating optimal trajectories cites this paper.

GCImOpt: Learning efficient goal-conditioned policies by imitating optimal trajectories Learning to Reach Goals via Iterated Supervised Learning

Reference 3

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arxiv_id, observed 2026-05-11T19:36:14.937117Z

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.

source=pdf_text observed=2026-05-08T11:26:22.149056Z digest=sha256:c251b25c2e4a3e2b7a93b0e7f3c25e9913e6f8d88e2e1989185971ef7131efe6

Observation 9cd5e1be-e9e7-426e-89cb-ac5bc540f741 · inbound

Refining Compositional Diffusion for Reliable Long-Horizon Planning cites this paper.

Refining Compositional Diffusion for Reliable Long-Horizon Planning Learning to Reach Goals via Iterated Supervised Learning

Reference 24

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arxiv_id, observed 2026-05-11T17:11:18.892607Z

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.

source=pdf_text observed=2026-05-08T17:47:58.141126Z digest=sha256:c753ea1894a236a447ac71595ec5f3af27a98e3d26e4bc7f9de4dfd23e1ef7d2

Observation 1f251c31-f01f-44ec-bb2d-a33d443c71a1 · inbound

Predictive but Not Plannable: RC-aux for Latent World Models cites this paper.

Predictive but Not Plannable: RC-aux for Latent World Models Learning to Reach Goals via Iterated Supervised Learning

Reference 13

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arxiv_id, observed 2026-05-11T03:50:57.291741Z

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.

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Observation eedc35fc-db0c-491c-b15a-4d9bf6b32975 · inbound

Multi-scale Predictive Representations for Goal-conditioned Reinforcement Learning cites this paper.

Multi-scale Predictive Representations for Goal-conditioned Reinforcement Learning Learning to Reach Goals via Iterated Supervised Learning

Reference 13

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arxiv_id, observed 2026-05-12T07:16:26.289713Z

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.

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Observation 0c3561a8-9ed5-4b13-b510-7f5d4cfafe5b · inbound

stable-worldmodel: A Platform for Reproducible World Modeling Research and Evaluation cites this paper.

stable-worldmodel: A Platform for Reproducible World Modeling Research and Evaluation Learning to Reach Goals via Iterated Supervised Learning

Reference 69

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arxiv_id, observed 2026-05-22T09:01:20.332386Z

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

source=pdf_text observed=2026-05-22T08:57:19.834179Z digest=sha256:ee6df8a87f4dbcc8d0217a27a1f08d3fc201a7cfe5ad7f3eac10f8321505bc69

Observation eaa4089d-ea58-428a-b5cd-a7c926920358 · inbound

Goal-Conditioned Agents that Learn Everything All at Once cites this paper.

Goal-Conditioned Agents that Learn Everything All at Once Learning to Reach Goals via Iterated Supervised Learning

Reference 47

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arxiv_id, observed 2026-05-25T05:00:21.639083Z

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.

source=arxiv_source observed=2026-05-25T04:59:48.867927Z digest=sha256:bca246a467aca22eaab04fd9a9cae530f6c9f9903b188979cab8598ef997af19

Observation 44fae367-5737-45ed-a48a-14de6a58cc3e · inbound

Goal Sets, Not Goal States: Queryable Robot Goals through Goal-Set Hindsight Relabeling cites this paper.

Goal Sets, Not Goal States: Queryable Robot Goals through Goal-Set Hindsight Relabeling Learning to Reach Goals via Iterated Supervised Learning

Reference 6

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arxiv_id, observed 2026-07-03T02:07:34.180587Z

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

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Observation 35711ff6-2e43-42c2-9206-285dde74bc2e · inbound

Energy-based Compositional Diffusion Planning cites this paper.

Energy-based Compositional Diffusion Planning Learning to Reach Goals via Iterated Supervised Learning

Reference 9

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arxiv_id, observed 2026-07-04T06:49:37.767185Z

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

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Observation f9624c5c-ee09-4152-827d-13cbb574d786 · inbound

Spinning Straw into Gold: Relabeling LLM Agent Trajectories in Hindsight for Successful Demonstrations cites this paper.

Spinning Straw into Gold: Relabeling LLM Agent Trajectories in Hindsight for Successful Demonstrations Learning to Reach Goals via Iterated Supervised Learning

Reference 22

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Unavailable: canonical work link unavailable.

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Observation 9583241c-4031-4b02-b8df-99f34b72dffa · inbound

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

Reinforcement Learning: From Algorithms To Foundation Models Learning to Reach Goals via Iterated Supervised Learning

Reference 163

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Observation d74aa734-1585-4a62-9ec3-50ea4030e825 · inbound

VisualPatchWorld: Code World Models as Latent Structured Representations for Planning cites this paper.

VisualPatchWorld: Code World Models as Latent Structured Representations for Planning Learning to Reach Goals via Iterated Supervised Learning

Reference 5

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Observation 21c83659-5f2d-4b0d-815c-58825c68a30d · inbound

Temporal-Distance JEPA: Plan-Aware Representation Learning for Latent World Model Predictive Control cites this paper.

Temporal-Distance JEPA: Plan-Aware Representation Learning for Latent World Model Predictive Control Learning to Reach Goals via Iterated Supervised Learning

Reference 5

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Observation c44246e3-72aa-4150-9137-819f79b53e22 · inbound

INTACT: Isomorphic Intent-to-Action Learning for Search-Free World Models cites this paper.

INTACT: Isomorphic Intent-to-Action Learning for Search-Free World Models Learning to Reach Goals via Iterated Supervised Learning

Reference 18

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