ARXIV:2602.20809 · REINFORCEMENT LEARNING · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE

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

Regret-Guided Search Control for Efficient Learning in AlphaZero

arXiv

Enhance AlphaZero's learning efficiency with Regret-Guided Search Control for improved gaming AI performance.

Blocked on Code›Score7.0Evidence unverified

Opportunity summary

Pain Enhance AlphaZero's learning efficiency with Regret-Guided Search Control for improved gaming AI performance.

Evidence 0 refs | 0 sources | 17% coverage

Blocker Evidence unverified

Open Build Read PDF Signal Canvas Track

PROBLEM

Enhance AlphaZero's learning efficiency with Regret-Guided Search Control for improved gaming AI performance. While RL agents require extensive self-play games to extract useful signals, humans often need only a few games, improving rapidly by…

METHOD

Full abstract

Reinforcement learning (RL) agents achieve remarkable performance but remain far less learning-efficient than humans. While RL agents require extensive self-play games to extract useful signals, humans often need only a few games, improving rapidly by repeatedly revisiting states where mistakes occurred. This idea, known as search control, aims to restart from valuable states rather than always from the initial state. In AlphaZero, prior work Go-Exploit applies this idea by sampling past states from self-play or search trees, but it treats all states equally, regardless of their learning potential. We propose Regret-Guided Search Control (RGSC), which extends AlphaZero with a regret network that learns to identify high-regret states, where the agent's evaluation diverges most from the actual outcome. These states are collected from both self-play trajectories and MCTS nodes, stored in a prioritized regret buffer, and reused as new starting positions. Across 9x9 Go, 10x10 Othello, and 11x11 Hex, RGSC outperforms AlphaZero and Go-Exploit by an average of 77 and 89 Elo, respectively. When training on a well-trained 9x9 Go model, RGSC further improves the win rate against KataGo from 69.3% to 78.2%, while both baselines show no improvement. These results demonstrate that RGSC provides an effective mechanism for search control, improving both efficiency and robustness of AlphaZero training. Our code is available at https://rlg.iis.sinica.edu.tw/papers/rgsc.

RESULT

ScienceToStartup currently rates this 7.0/10 on the public viability pass. Reinforcement learning (RL) agents achieve remarkable performance but remain far less learning-efficient than humans.

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

PainEnhance AlphaZero's learning efficiency with Regret-Guided Search Control for improved gaming AI performance.

Evidence0 refs | 0 sources | 17% coverage

Blockermissing authors

Analysis summary

Enhance AlphaZero's learning efficiency with Regret-Guided Search Control for improved gaming AI performance.

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

ARXIV:2602.20809 · REINFORCEMENT LEARNING · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE

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

Regret-Guided Search Control for Efficient Learning in AlphaZero

arXiv

Enhance AlphaZero's learning efficiency with Regret-Guided Search Control for improved gaming AI performance.

Blocked on Code›Score7.0Evidence unverified

Opportunity summary

Pain Enhance AlphaZero's learning efficiency with Regret-Guided Search Control for improved gaming AI performance.

Evidence 0 refs | 0 sources | 17% coverage

Blocker Evidence unverified

Open Build Read PDF Signal Canvas Track

PROBLEM

METHOD

Full abstract

RESULT

ScienceToStartup currently rates this 7.0/10 on the public viability pass. Reinforcement learning (RL) agents achieve remarkable performance but remain far less learning-efficient than humans.

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

PainEnhance AlphaZero's learning efficiency with Regret-Guided Search Control for improved gaming AI performance.

Evidence0 refs | 0 sources | 17% coverage

Blockermissing authors

Analysis summary

Enhance AlphaZero's learning efficiency with Regret-Guided Search Control for improved gaming AI performance.

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

Paper Pack

10.48550/arXiv.2602.20809

Regret-Guided Search Control for Efficient Learning in AlphaZero

Enhance AlphaZero's learning efficiency with Regret-Guided Search Control for improved gaming AI performance.

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 fallback28 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

ScienceToStartup currently rates this 7.0/10 on the public viability pass. Reinforcement learning (RL) agents achieve remarkable performance but remain far less learning-efficient than humans.

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
Enhance AlphaZero's learning efficiency with Regret-Guided Search Control for improved gaming AI performance. While RL agents require extensive self-play games to extract useful signals, humans often need only a few games, improving rapidly by repeatedly revisiting states where mistakes occurred.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial
Evidencepartial
Reinforcement learning (RL) agents achieve remarkable performance but remain far less learning-efficient than humans. While RL agents require extensive self-play games to extract useful signals, humans often need only a few games, improving rapidly by repeatedly revisiting states where mistakes occurred.
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. Reinforcement learning (RL) agents achieve remarkable performance but remain far less learning-efficient than humans.
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

Enhance AlphaZero's learning efficiency with Regret-Guided Search Control for improved gaming AI performance.

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 2602.20809 yet. That is a visibility signal, not a blank module: the monitor is watching the public channels below.

Hacker News

Not indexed yet

Bluesky

Not indexed yet

PDF

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

References(28)

MiniZero: Comparative Analysis of AlphaZero and MuZero on Go, Othello, and Atari Games

2023Ti-Rong Wu, Hung Guei et al.

Targeted Search Control in AlphaZero for Effective Policy Improvement

2023Alexandre Trudeau, Michael H. Bowling

Meta-Reinforcement Learning via Exploratory Task Clustering

2023Zhendong Chu, Hongning Wang

Expediting Self-Play Learning in AlphaZero-Style Game-Playing Agents

2023Y. Björnsson, R. Jónsson et al.

Few-Shot Preference Learning for Human-in-the-Loop RL

2022Joey Hejna, Dorsa Sadigh

Jump-Start Reinforcement Learning

2022Ikechukwu Uchendu, Ted Xiao et al.

Evolving Curricula with Regret-Based Environment Design

2022Jack Parker-Holder, Minqi Jiang et al.

Planning in Stochastic Environments with a Learned Model

2022Ioannis Antonoglou, Julian Schrittwieser et al.

Human-Level Reinforcement Learning through Theory-Based Modeling, Exploration, and Planning

2021Pedro Tsividis, J. Loula et al.

Learning and Planning in Complex Action Spaces

2021T. Hubert, Julian Schrittwieser et al.

Emergent Complexity and Zero-shot Transfer via Unsupervised Environment Design

2020Michael Dennis, Natasha Jaques et al.

Prioritized Level Replay

2020Minqi Jiang, Edward Grefenstette et al.

Exploring Restart Distributions.

2020Arash Tavakoli, Vitaly Levdik et al.

First return, then explore

2020Adrien Ecoffet, Joost Huizinga et al.

Mastering Atari, Go, chess and shogi by planning with a learned model

2019Julian Schrittwieser, Ioannis Antonoglou et al.

Ludii - The Ludemic General Game System

2019Éric Piette, D. Soemers et al.

Accelerating Self-Play Learning in Go

2019David J. Wu

A general reinforcement learning algorithm that masters chess, shogi, and Go through self-play

2018David Silver, T. Hubert et al.

Unsupervised Meta-Learning for Reinforcement Learning

2018Abhishek Gupta, Benjamin Eysenbach et al.

Mastering the game of Go without human knowledge

2017David Silver, Julian Schrittwieser et al.

Showing 20 of 28 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 ThisLearning to Search and Searching to Learn for Generalization in Planning

0.0

Builds On ThisData-Augmented Game Starts for Accelerating Self-Play Exploration in Imperfect Information Games

4.0

Builds On ThisDiscovering Multiagent Learning Algorithms with Large Language Models

5.0

Builds On ThisA Regret Minimization Framework on Preference Learning in Large Language Models

0.0

Builds On ThisBeyond Stochastic Exploration: What Makes Training Data Valuable for Agentic Search

4.0

Commercially relevant

Higher ViabilityMeta-Reinforcement Learning with Self-Reflection for Agentic Search

8.0

Higher ViabilityCuSearch: Curriculum Rollout Sampling via Search Depth for Agentic RAG

8.0

Higher ViabilityRetroAgent: From Solving to Evolving via Retrospective Dual Intrinsic Feedback

8.0

Conflicting

Competing ApproachOracle-Guided Soft Shielding for Safe Move Prediction in Chess

7.0

Competing ApproachTSR: Trajectory-Search Rollouts for Multi-Turn RL of LLM Agents

3.0

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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Get the weekly shortlist of commercializable papers, benchmark movers, and proof receipts that matter for product execution.

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: 2602.20809
Route: /paper/regret-guided-search-control-for-efficient-learning-in-alphazero
Active tab: read
Artifact: regret-guided-search-control-for-efficient-learning-in-alphazero

Available agents

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

API/MCP endpoints

REST paper pack API/api/v1/paper/regret-guided-search-control-for-efficient-learning-in-alphazero/paper-pack
REST build passport API/api/v1/paper/regret-guided-search-control-for-efficient-learning-in-alphazero/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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  "canonical_route": "/paper/regret-guided-search-control-for-efficient-learning-in-alphazero",
  "active_tab": "synced from current hash by the drawer client",
  "selected_artifact": "regret-guided-search-control-for-efficient-learning-in-alphazero",
  "endpoints": {
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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/regret-guided-search-control-for-efficient-learning-in-alphazero

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

Regret-Guided Search Control for Efficient Learning in AlphaZero

Canonical ID regret-guided-search-control-for-efficient-learning-in-alphazero | Route /paper/regret-guided-search-control-for-efficient-learning-in-alphazero

REST example

curl https://sciencetostartup.com/api/v1/agent-handoff/paper/regret-guided-search-control-for-efficient-learning-in-alphazero

MCP example

{
  "tool": "get_paper",
  "arguments": {
    "arxiv_id": "2602.20809"
  }
}

source_context

{
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  "mode": "paper",
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  "paper_ref": "regret-guided-search-control-for-efficient-learning-in-alphazero",
  "topic_slug": null,
  "benchmark_ref": null,
  "dataset_ref": null
}

Buildability Receipt

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

Paper proof page receipt window

Watch and verify: Regret-Guided Search Control for Efficient Learning in AlphaZero

/buildability/regret-guided-search-control-for-efficient-learning-in-alphazero

Watchwatch

Subject: Regret-Guided Search Control for Efficient Learning in AlphaZero

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/regret-guided-search-control-for-efficient-learning-in-alphazero

Paper ref

regret-guided-search-control-for-efficient-learning-in-alphazero

arXiv id

2602.20809

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

8be6a97e844a44cb3aab1e1dd6ffc7f0db9f492afb780c2804f80fac6c9677c5

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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Regret-Guided Search Control for Efficient Learning in AlphaZero

Regret-Guided Search Control for Efficient Learning in AlphaZero

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