ARXIV:2602.17632 · REINFORCEMENT LEARNING · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE

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

SMAC: Score-Matched Actor-Critics for Robust Offline-to-Online Transfer

Q: What is the startup potential of "SMAC: Score-Matched Actor-Critics for Robust Offline-to-Onli"?

Optimize actor-critics to seamlessly transition from offline pre-training to online fine-tuning without performance drops.

Q: What products could be built from this research?

Commercialize SMAC as a software tool or library that integrates with existing RL frameworks, targeting industries that rely on continuous model updates and fine-tuning, such as logistics and manufacturing robotics.

Q: What are the practical use cases?

Develop an RL-based platform for autonomous systems where models pre-trained on past data can be fine-tuned online in new environments without a drop in performance, crucial for sectors like robotics and autonomous vehicles.

Q: What industries could this research disrupt?

SMAC could replace existing RL solutions that require extensive retraining after offline pre-training, offering a more efficient and performance-stable integration in real-world applications.

arXiv

SMAC provides a method for seamless offline-to-online Reinforcement Learning transfer, minimizing performance drops during online fine-tuning.

Blocked on Code›Score5.0Evidence unverified

Opportunity summary

Pain SMAC provides a method for seamless offline-to-online Reinforcement Learning transfer, minimizing performance drops during online fine-tuning.

Evidence 0 refs | 0 sources | 17% coverage

Blocker Evidence unverified

Open Build Read PDF Signal Canvas Track

PROBLEM

SMAC provides a method for seamless offline-to-online Reinforcement Learning transfer, minimizing performance drops during online fine-tuning. We provide evidence consistent with the hypothesis that, in the loss landscape, offline maxima for prior algorithms and…

METHOD

Full abstract

Modern offline Reinforcement Learning (RL) methods find performant actor-critics, however, fine-tuning these actor-critics online with value-based RL algorithms typically causes immediate drops in performance. We provide evidence consistent with the hypothesis that, in the loss landscape, offline maxima for prior algorithms and online maxima are separated by low-performance valleys that gradient-based fine-tuning traverses. Following this, we present Score Matched Actor-Critic (SMAC), an offline RL method designed to learn actor-critics that transition to online value-based RL algorithms with no drop in performance. SMAC avoids valleys between offline and online maxima by regularizing the Q-function during the offline phase to respect a first-order derivative equality between the score of the policy and action-gradient of the Q-function. We experimentally demonstrate that SMAC converges to offline maxima that are connected to better online maxima via paths with monotonically increasing reward found by first-order optimization. SMAC achieves smooth transfer to Soft Actor-Critic and TD3 in 6/6 D4RL tasks. In 4/6 environments, it reduces regret by 34-58% over the best baseline.

RESULT

ScienceToStartup currently rates this 5.0/10 on the public viability pass. We experimentally demonstrate that SMAC converges to offline maxima that are connected to better online maxima via paths with monotonically increasing reward found by…

WHY NOW

Reinforcement Learning moved forward this cycle; last verified April 2026. Public score 5.0/10.

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

Opportunity summary

Score5.0

PainSMAC provides a method for seamless offline-to-online Reinforcement Learning transfer, minimizing performance drops during online fine-tuning.

Evidence0 refs | 0 sources | 17% coverage

Blockermissing authors

Analysis summary

SMAC provides a method for seamless offline-to-online Reinforcement Learning transfer, minimizing performance drops during online fine-tuning.

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

ARXIV:2602.17632 · REINFORCEMENT LEARNING · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE

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

SMAC: Score-Matched Actor-Critics for Robust Offline-to-Online Transfer

arXiv

SMAC provides a method for seamless offline-to-online Reinforcement Learning transfer, minimizing performance drops during online fine-tuning.

Blocked on Code›Score5.0Evidence unverified

Opportunity summary

Pain SMAC provides a method for seamless offline-to-online Reinforcement Learning transfer, minimizing performance drops during online fine-tuning.

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 5.0/10.

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

Opportunity summary

Score5.0

PainSMAC provides a method for seamless offline-to-online Reinforcement Learning transfer, minimizing performance drops during online fine-tuning.

Evidence0 refs | 0 sources | 17% coverage

Blockermissing authors

Analysis summary

SMAC provides a method for seamless offline-to-online Reinforcement Learning transfer, minimizing performance drops during online fine-tuning.

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

Paper Pack

10.48550/arXiv.2602.17632

SMAC: Score-Matched Actor-Critics for Robust Offline-to-Online Transfer

SMAC provides a method for seamless offline-to-online Reinforcement Learning transfer, minimizing performance drops during online fine-tuning.

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 fallback52 indexednot indexed

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

Viability

5.0

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 5.0/10.

Claim map

Abstract-backed public claims while anchored extraction refreshes.

Strong 0Mixed 0Weak 4

Evidencepartial
SMAC provides a method for seamless offline-to-online Reinforcement Learning transfer, minimizing performance drops during online fine-tuning. We provide evidence consistent with the hypothesis that, in the loss landscape, offline maxima for prior algorithms and online maxima are separated by low-performance valleys that gradient-based fine-tuning traverses.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial
Evidencepartial
Modern offline Reinforcement Learning (RL) methods find performant actor-critics, however, fine-tuning these actor-critics online with value-based RL algorithms typically causes immediate drops in performance. We provide evidence consistent with the hypothesis that, in the loss landscape, offline maxima for prior algorithms and online maxima are separated by low-performance valleys that gradient-based fine-tuning traverses.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial
Evidencepartial
ScienceToStartup currently rates this 5.0/10 on the public viability pass. We experimentally demonstrate that SMAC converges to offline maxima that are connected to better online maxima via paths with monotonically increasing reward found by first-order optimization.
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 5.0/10.
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

SMAC provides a method for seamless offline-to-online Reinforcement Learning transfer, minimizing performance drops during online fine-tuning.

Segment

Reinforcement Learning

Adoption evidence

No public code link in the paper record yet

Commercial read

5.0/10 public viability

Direct

not classified

Adjacent

not classified

Substitute

not classified

Unknown

not classified

Buzz

No indexed public discussion is attached to 2602.17632 yet. That is a visibility signal, not a blank module: the monitor is watching the public channels below.

Hacker News

Not indexed yet

Bluesky

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PDF

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

References(52)

What Really Matters in Matrix-Whitening Optimizers?

2025Kevin Frans, Pieter Abbeel et al.

EXPO: Stable Reinforcement Learning with Expressive Policies

2025Perry Dong, Qiyang Li et al.

SmolVLA: A Vision-Language-Action Model for Affordable and Efficient Robotics

2025Mustafa Shukor, Dana Aubakirova et al.

GR00T N1: An Open Foundation Model for Generalist Humanoid Robots

2025Nvidia, Johan Bjorck et al.

Efficient Online Reinforcement Learning Fine-Tuning Need Not Retain Offline Data

2024Zhiyuan Zhou, Andy Peng et al.

SimBa: Simplicity Bias for Scaling Up Parameters in Deep Reinforcement Learning

2024Hojoon Lee, Dongyoon Hwang et al.

CleanDiffuser: An Easy-to-use Modularized Library for Diffusion Models in Decision Making

2024Zibin Dong, Yifu Yuan et al.

A Perspective of Q-value Estimation on Offline-to-Online Reinforcement Learning

2023Yinmin Zhang, Jie Liu et al.

Towards Robust Offline-to-Online Reinforcement Learning via Uncertainty and Smoothness

2023Xiaoyu Wen, Xudong Yu et al.

Actor-Critic Alignment for Offline-to-Online Reinforcement Learning

2023Zishun Yu, Xinhua Zhang

A Simple Unified Uncertainty-Guided Framework for Offline-to-Online Reinforcement Learning

2023Siyuan Guo, Yanchao Sun et al.

ENOTO: Improving Offline-to-Online Reinforcement Learning with Q-Ensembles

2023Kai-Wen Zhao, Jianye Hao et al.

PROTO: Iterative Policy Regularized Offline-to-Online Reinforcement Learning

2023Jianxiong Li, Xiao Hu et al.

IDQL: Implicit Q-Learning as an Actor-Critic Method with Diffusion Policies

2023Philippe Hansen-Estruch, Ilya Kostrikov et al.

Adaptive Policy Learning for Offline-to-Online Reinforcement Learning

2023Han Zheng, Xufang Luo et al.

Cal-QL: Calibrated Offline RL Pre-Training for Efficient Online Fine-Tuning

2023Mitsuhiko Nakamoto, Yuexiang Zhai et al.

Aligning Human and Robot Representations

2023Andreea Bobu, Andi Peng et al.

Policy Expansion for Bridging Offline-to-Online Reinforcement Learning

2023Haichao Zhang, Weiwen Xu et al.

Is Conditional Generative Modeling all you need for Decision-Making?

2022Anurag Ajay, Yilun Du et al.

Same Pre-training Loss, Better Downstream: Implicit Bias Matters for Language Models

2022Hong Liu, Sang Michael Xie et al.

Showing 20 of 52 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

none indexed

Extension

Builds On ThisBridging the Gap: Enabling Soft Actor Critic for High Performance Legged Locomotion

0.0

Commercially relevant

Higher ViabilityDiscrete Flow Matching for Offline-to-Online Reinforcement Learning

6.0

Higher ViabilityFlashSAC: Fast and Stable Off-Policy Reinforcement Learning for High-Dimensional Robot Control

7.0

Higher ViabilityLearning Decentralized LLM Collaboration with Multi-Agent Actor Critic

7.0

Higher ViabilityRankQ: Offline-to-Online Reinforcement Learning via Self-Supervised Action Ranking

7.0

Higher ViabilityAndroid Coach: Improve Online Agentic Training Efficiency with Single State Multiple Actions

7.0

Conflicting

Competing ApproachAdapting Critic Match Loss Landscape Visualization to Off-policy Reinforcement Learning

3.0

Competing ApproachVisualizing Critic Match Loss Landscapes for Interpretation of Online Reinforcement Learning Control Algorithms

4.0

Competing ApproachA Recipe for Stable Offline Multi-agent Reinforcement Learning

1.0

Competing ApproachSecond-Order Actor-Critic Methods for Discounted MDPs via Policy Hessian Decomposition

1.0

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: 2602.17632
Route: /paper/smac-score-matched-actor-critics-for-robust-offline-to-online-transfer
Active tab: read
Artifact: smac-score-matched-actor-critics-for-robust-offline-to-online-transfer

Available agents

Read extractor
Build planner
Track monitor
Competitive mapper
Related-paper scout

API/MCP endpoints

REST paper pack API/api/v1/paper/smac-score-matched-actor-critics-for-robust-offline-to-online-transfer/paper-pack
REST build passport API/api/v1/paper/smac-score-matched-actor-critics-for-robust-offline-to-online-transfer/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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}

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/smac-score-matched-actor-critics-for-robust-offline-to-online-transfer

stale

Proof freshness: stale
Proof status: unverified
Display score: 5/10
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

SMAC: Score-Matched Actor-Critics for Robust Offline-to-Online Transfer

Canonical ID smac-score-matched-actor-critics-for-robust-offline-to-online-transfer | Route /paper/smac-score-matched-actor-critics-for-robust-offline-to-online-transfer

REST example

curl https://sciencetostartup.com/api/v1/agent-handoff/paper/smac-score-matched-actor-critics-for-robust-offline-to-online-transfer

MCP example

{
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  "arguments": {
    "arxiv_id": "2602.17632"
  }
}

source_context

{
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  "mode": "paper",
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  "topic_slug": null,
  "benchmark_ref": null,
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}

Buildability Receipt

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

Paper proof page receipt window

Watch and verify: SMAC: Score-Matched Actor-Critics for Robust Offline-to-Online Transfer

/buildability/smac-score-matched-actor-critics-for-robust-offline-to-online-transfer

Watchwatch

Subject: SMAC: Score-Matched Actor-Critics for Robust Offline-to-Online Transfer

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/smac-score-matched-actor-critics-for-robust-offline-to-online-transfer

Paper ref

smac-score-matched-actor-critics-for-robust-offline-to-online-transfer

arXiv id

2602.17632

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

99973321f5e9e67a1b2147ea6a9332c892cd364729097d0164aa4e9374e1c300

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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SMAC: Score-Matched Actor-Critics for Robust Offline-to-Online Transfer

SMAC: Score-Matched Actor-Critics for Robust Offline-to-Online Transfer

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