ARXIV:2602.03389 · REINFORCEMENT LEARNING · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE

VerifiedSource: PDF linkedPartialPaperPack: 3 of 4 citation fields filledMissingMissing fields: authorsPartialProof: unverified proof status

Chain-of-Goals Hierarchical Policy for Long-Horizon Offline Goal-Conditioned RL

arXiv

Revolutionizing long-horizon offline goal-conditioned RL with sequential subgoal autoregression.

Blocked on Code›Score7.0Evidence unverified

Opportunity summary

Pain Revolutionizing long-horizon offline goal-conditioned RL with sequential subgoal autoregression.

Evidence 0 refs | 0 sources | 17% coverage

Blocker Evidence unverified

Open Build Read PDF Signal Canvas Track

PROBLEM

Revolutionizing long-horizon offline goal-conditioned RL with sequential subgoal autoregression. While hierarchical approaches mitigate this issue by decomposing tasks, most existing methods rely on separate high- and low-level networks and generate only a single intermediate…

METHOD

Full abstract

Offline goal-conditioned reinforcement learning remains challenging for long-horizon tasks. While hierarchical approaches mitigate this issue by decomposing tasks, most existing methods rely on separate high- and low-level networks and generate only a single intermediate subgoal, making them inadequate for complex tasks that require coordinating multiple intermediate decisions. To address this limitation, we draw inspiration from the chain-of-thought paradigm and propose the Chain-of-Goals Hierarchical Policy (CoGHP), a novel framework that reformulates hierarchical decision-making as autoregressive sequence modeling within a unified architecture. Given a state and a final goal, CoGHP autoregressively generates a sequence of latent subgoals followed by the primitive action, where each latent subgoal acts as a reasoning step that conditions subsequent predictions. To implement this efficiently, we pioneer the use of an MLP-Mixer backbone, which supports cross-token communication and captures structural relationships among state, goal, latent subgoals, and action. Across challenging navigation and manipulation benchmarks, CoGHP consistently outperforms strong offline baselines, demonstrating improved performance on long-horizon tasks.

RESULT

ScienceToStartup currently rates this 7.0/10 on the public viability pass. To implement this efficiently, we pioneer the use of an MLP-Mixer backbone, which supports cross-token communication and captures structural relationships among state, goal, latent…

WHY NOW

Reinforcement Learning moved forward this cycle; last verified April 2026. Public score 7.0/10.

Continue into Read for claims, analysis, references, and neighboring papers.

Opportunity summary

Score7.0

PainRevolutionizing long-horizon offline goal-conditioned RL with sequential subgoal autoregression.

Evidence0 refs | 0 sources | 17% coverage

Blockermissing authors

Analysis summary

Revolutionizing long-horizon offline goal-conditioned RL with sequential subgoal autoregression.

VerifiedSource: PDF linkedPartialPaperPack: 3 of 4 citation fields filledMissingMissing fields: authorsPartialProof: unverified proof status

ARXIV:2602.03389 · REINFORCEMENT LEARNING · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE

VerifiedSource: PDF linkedPartialPaperPack: 3 of 4 citation fields filledMissingMissing fields: authorsPartialProof: unverified proof status

Chain-of-Goals Hierarchical Policy for Long-Horizon Offline Goal-Conditioned RL

arXiv

Revolutionizing long-horizon offline goal-conditioned RL with sequential subgoal autoregression.

Blocked on Code›Score7.0Evidence unverified

Opportunity summary

Pain Revolutionizing long-horizon offline goal-conditioned RL with sequential subgoal autoregression.

Evidence 0 refs | 0 sources | 17% coverage

Blocker Evidence unverified

Open Build Read PDF Signal Canvas Track

PROBLEM

METHOD

Full abstract

RESULT

WHY NOW

Reinforcement Learning moved forward this cycle; last verified April 2026. Public score 7.0/10.

Continue into Read for claims, analysis, references, and neighboring papers.

Opportunity summary

Score7.0

PainRevolutionizing long-horizon offline goal-conditioned RL with sequential subgoal autoregression.

Evidence0 refs | 0 sources | 17% coverage

Blockermissing authors

Analysis summary

Revolutionizing long-horizon offline goal-conditioned RL with sequential subgoal autoregression.

VerifiedSource: PDF linkedPartialPaperPack: 3 of 4 citation fields filledMissingMissing fields: authorsPartialProof: unverified proof status

Paper Pack

10.48550/arXiv.2602.03389

Chain-of-Goals Hierarchical Policy for Long-Horizon Offline Goal-Conditioned RL

Revolutionizing long-horizon offline goal-conditioned RL with sequential subgoal autoregression.

Abstract

Source availability

PDF linked

The paper record includes a public PDF URL.

Extraction status

Derived fallback

Read summaries are estimated from adjacent metadata, not verified extraction rows.

Proof status

unverified

0 refs; 0 sources; 17% coverage.

What was readable

linkedon filenot materializedderived fallback44 indexednot indexed

Derived fallback: Estimated from adjacent evidence; not verified from source.

Viability

7.0

Time to MVP

MVP estimate missing

Commercial

No commercial flags on file

Export

Preparing verified analysis

lens / founder

PROBLEM

METHOD

RESULT

WHY NOW

Reinforcement Learning moved forward this cycle; last verified April 2026. Public score 7.0/10.

Claim map

Abstract-backed public claims while anchored extraction refreshes.

Strong 0Mixed 0Weak 4

Evidencepartial
Revolutionizing long-horizon offline goal-conditioned RL with sequential subgoal autoregression. While hierarchical approaches mitigate this issue by decomposing tasks, most existing methods rely on separate high- and low-level networks and generate only a single intermediate subgoal, making them inadequate for complex tasks that require coordinating multiple intermediate decisions.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial
Evidencepartial
Offline goal-conditioned reinforcement learning remains challenging for long-horizon tasks. While hierarchical approaches mitigate this issue by decomposing tasks, most existing methods rely on separate high- and low-level networks and generate only a single intermediate subgoal, making them inadequate for complex tasks that require coordinating multiple intermediate decisions.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial
Evidencepartial
ScienceToStartup currently rates this 7.0/10 on the public viability pass. To implement this efficiently, we pioneer the use of an MLP-Mixer backbone, which supports cross-token communication and captures structural relationships among state, goal, latent subgoals, and action.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial
Evidencepartial
Reinforcement Learning moved forward this cycle; last verified April 2026. Public score 7.0/10.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial

Constellation map

Paper-native neighborhood for concepts, methods, materials, markets, and competitors. Missing lanes stay labeled instead of disappearing behind commercialization gates.

Open full Signal Canvas

Concepts

not indexed

Methods

Materials

PDF linked

Markets

Reinforcement Learning

Competitors

not indexed

Competitive landscape

Revolutionizing long-horizon offline goal-conditioned RL with sequential subgoal autoregression.

Segment

Reinforcement Learning

Adoption evidence

No public code link in the paper record yet

Commercial read

7.0/10 public viability

Direct

not classified

Adjacent

not classified

Substitute

not classified

Unknown

not classified

Buzz

No indexed public discussion is attached to 2602.03389 yet. That is a visibility signal, not a blank module: the monitor is watching the public channels below.

Hacker News

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Bluesky

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PDF

Preview the source document here, or use the hero PDF action for a new tab.

References(44)

Reference metadata pending (8d2a34208dcdacfdb286ba0c5ef35578865e2229)

Reference metadata pending (2d7f3a99e916fc80ff890d109699f9682253e66d)

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Reference metadata pending (2a37336354499c3170a4aaf6e72c99ca7ab04155)

Reference metadata pending (7362b81b808ebafd403eaec6f60a124340d68d71)

Reference metadata pending (56b545002ebb897de167a2de1603f06ab8f6bb0a)

Reference metadata pending (1772de89a739b3abedd5ade49aa82eb378cec3c0)

Reference metadata pending (4b1278b2266ce5009e70f2efe85ccff87350de9c)

Reference metadata pending (f18587247e4769ad0efd96a0286b012d856ba214)

Reference metadata pending (00cb69a9f280317d1c59ac5827551ee9b10642b8)

Reference metadata pending (abec4dcbff2c6be5578ee5bf6c96347e7901b6a0)

Reference metadata pending (1ad85b1a902cd37c855dbedf5bd17c536628b611)

Reference metadata pending (59694c8dce4f13db2f486eb8102459a3f7c23da6)

Reference metadata pending (ca137b4f6ba25ca2f26952044c6a2d06ae3e607f)

Reference metadata pending (90350aa626bed47b02d0c162462e5b0ca82be6b2)

Reference metadata pending (bd3a0bbabae3260098e06bfb615147fb6d34e55a)

Reference metadata pending (748c9aa5a31f279fa07b84238aa5ba748e9df40d)

Reference metadata pending (8e9d84a7b2db57adda8d639c6d54c8977ef10761)

Reference metadata pending (53dcf467fbded741dd08902d4203a9b57e889c87)

Reference metadata pending (cb3631f12b4465f4396380b61a651f0c74763480)

Showing 20 of 44 references

CITED BY

No citing papers are indexed in the public S2S graph yet. This is an explicit zero-signal state, not a hidden lookup.

Foundation

Prior WorkIterative On-Policy Refinement of Hierarchical Diffusion Policies for Language-Conditioned Manipulation

7.0

Prior WorkHierarchical Reinforcement Learning with Augmented Step-Level Transitions for LLM Agents

7.0

Extension

Builds On ThisHiPER: Hierarchical Reinforcement Learning with Explicit Credit Assignment for Large Language Model Agents

6.0

Builds On ThisPosterior Optimization with Clipped Objective for Bridging Efficiency and Stability in Generative Policy Learning

4.0

Builds On ThisMPCoT: Reward-Guided Multi-Path Latent Reasoning for Test-Time Scalable Vision-Language-Action

0.0

Commercially relevant

Higher ViabilityLearning and Reusing Policy Decompositions for Hierarchical Generalized Planning with LLM Agents

8.0

Conflicting

Competing ApproachHierarchy-of-Groups Policy Optimization for Long-Horizon Agentic Tasks

6.0

Competing ApproachData-Efficient Hierarchical Goal-Conditioned Reinforcement Learning via Normalizing Flows

5.0

Competing ApproachAction-Sufficient Goal Representations

4.0

Competing ApproachExploiting Local Dynamics Regularity for Reusable Skills in Offline Hierarchical RL

4.0

Related Resources

Just-In-Time Reinforcement Learning(glossary)
Multi-Agent Reinforcement Learning(glossary)
Multi-Agent Test-Time Reinforcement Learning (MATTRL)(glossary)
How does PRISM improve reinforcement learning?(question)
What is the significance of reinforcement learning in AI?(question)
How does RetroAgent improve reinforcement learning?(question)
Reinforcement Learning – Use Cases(use_case)

Owned Distribution

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Agent drawer

5 surfaces preserved for agents. Humans can ignore.

Developer contracts, payload previews, evidence maps, and run controls stay here instead of the Read, Build, and Track workspace.

Run context

Paper: 2602.03389
Route: /paper/chain-of-goals-hierarchical-policy-for-long-horizon-offline-goal-conditioned-rl
Active tab: read
Artifact: chain-of-goals-hierarchical-policy-for-long-horizon-offline-goal-conditioned-rl

Available agents

Read extractor
Build planner
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Competitive mapper
Related-paper scout

API/MCP endpoints

REST paper pack API/api/v1/paper/chain-of-goals-hierarchical-policy-for-long-horizon-offline-goal-conditioned-rl/paper-pack
REST build passport API/api/v1/paper/chain-of-goals-hierarchical-policy-for-long-horizon-offline-goal-conditioned-rl/build-passport
REST OpenAPI/api/openapi.json
MCP descriptor/api/mcp
MCP resourcesciencetostartup://surfaces/paper-workspace

Tool contracts

paper_packbuild_passportopportunity_kernelforesightsource_proofevidence_state

Payload preview

Inspect payload

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    "build_passport": "/api/v1/paper/chain-of-goals-hierarchical-policy-for-long-horizon-offline-goal-conditioned-rl/build-passport",
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}

Schema validation

paper-r2 contract: present
JSON-LD twin: SSR emitted
OpenAPI path parity: /api/openapi.json
MCP resource parity: paper-workspace

Job trace

queued: drawer opened by user action
running: inspect or copy payload
succeeded: payload available in SSR
failed: route errors appear in evidence cards

Evidence map

sources used: page freshness, source proof anchors, JSON-LD
missing sources: exposed by PaperPack and EvidenceState chips
derived fallbacks: marked unverified before handoff

Page Freshness

Canonical route, proof status, last verified, refs, sources, and coverage.

Page Freshness

Paper proof surface

Canonical route: /paper/chain-of-goals-hierarchical-policy-for-long-horizon-offline-goal-conditioned-rl

stale

Proof freshness: stale
Proof status: unverified
Display score: 7/10
Last proof check: 2026-04-02
Score updated: 2026-04-02
Score fresh until: 2026-05-02
References: 0
Source count: 0
Coverage: 17%

This page is showing the last landed evidence receipt and score bundle because the latest proof data is outside the freshness window.

OpenAlex: pending — this preprint is not yet indexed by OpenAlex.

Agent Handoff

Endpoint list, payload shape, route context, and copyable handoff data.

Agent Handoff

Chain-of-Goals Hierarchical Policy for Long-Horizon Offline Goal-Conditioned RL

Canonical ID chain-of-goals-hierarchical-policy-for-long-horizon-offline-goal-conditioned-rl | Route /paper/chain-of-goals-hierarchical-policy-for-long-horizon-offline-goal-conditioned-rl

REST example

curl https://sciencetostartup.com/api/v1/agent-handoff/paper/chain-of-goals-hierarchical-policy-for-long-horizon-offline-goal-conditioned-rl

MCP example

{
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  }
}

source_context

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  "route": "/paper/chain-of-goals-hierarchical-policy-for-long-horizon-offline-goal-conditioned-rl",
  "paper_ref": "chain-of-goals-hierarchical-policy-for-long-horizon-offline-goal-conditioned-rl",
  "topic_slug": null,
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}

Buildability Receipt

Verdict, compute envelope, blockers, signature state, and receipt links.

Paper proof page receipt window

Watch and verify: Chain-of-Goals Hierarchical Policy for Long-Horizon Offline Goal-Conditioned RL

/buildability/chain-of-goals-hierarchical-policy-for-long-horizon-offline-goal-conditioned-rl

Watchwatch

Subject: Chain-of-Goals Hierarchical Policy for Long-Horizon Offline Goal-Conditioned RL

Verdict

Watch

Verdict is Watch because viability or proof quality is intermediate and should be re-evaluated before execution.

Time to first demo

Insufficient data

No first-demo timestamp, owner estimate, or elapsed demo receipt is attached to this surface.

Compute envelope

Structured compute envelope

Insufficient data

No data, compute, hardware, memory, latency, dependency, or serving requirement receipt is attached.

Evidence ids

Receipt path

/buildability/chain-of-goals-hierarchical-policy-for-long-horizon-offline-goal-conditioned-rl

Paper ref

chain-of-goals-hierarchical-policy-for-long-horizon-offline-goal-conditioned-rl

arXiv id

2602.03389

Freshness

Generated at

2026-04-02T02:30:40.136Z

Evidence freshness

stale

Last verification

2026-04-02T02:30:40.136Z

Sources

References

Coverage

17%

Hash state

Lineage hash

2c5bfe4de9d63a6599127ba67dcb45867adbf654f6c49047d8ac933d498dad7f

Canonical opportunity-kernel lineage hash.

Signature state

External signature

unsigned_external

No founder, registry, pilot, or production-adoption signature is attached to this receipt.

Verification

not_verified

Verification is blocked until an external signature is provided.

Blockers

Missing: repo_url
Missing: references
Missing: proof_status
Missing: distribution_readiness_scores
Missing: paper_extraction_scorecards
Unknown: distribution readiness has not been computed yet
Unknown: proof verification has not been recorded yet

Verification pending / evidence receipt incomplete

repo_url

references

Missing proof, requirement, signature, approval, adoption, or telemetry fields are blockers and must not be inferred.

Open receipt API receipt Build Loop Signal Canvas Proof divergence Divergence API Brier outcomes API

Source Proof anchors

Visual citations from the paper document graph.

JSON-LD twin

The application/ld+json payload rendered for agents.

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Chain-of-Goals Hierarchical Policy for Long-Horizon Offline Goal-Conditioned RL

Chain-of-Goals Hierarchical Policy for Long-Horizon Offline Goal-Conditioned RL

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Claim map

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References(44)

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Subscribe to the weekly brief

Build artifacts

Brief

Experiment plan

Validation checklist

Scientific founder

Translational engineer

Domain operator

GTM lead

Regulatory/clinical advisor

Timeline