ARXIV:2603.10887 · REINFORCEMENT LEARNING · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE

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

Dynamics-Predictive Sampling for Active RL Finetuning of Large Reasoning Models

arXiv

Dynamics-Predictive Sampling accelerates RL finetuning for large reasoning models by efficiently selecting informative prompts.

Blocked on Code›Score7.0Evidence unverified

Opportunity summary

Pain Dynamics-Predictive Sampling accelerates RL finetuning for large reasoning models by efficiently selecting informative prompts.

Evidence 0 refs | 0 sources | 17% coverage

Blocker Evidence unverified

Open Build Read PDF Signal Canvas Track

PROBLEM

Dynamics-Predictive Sampling accelerates RL finetuning for large reasoning models by efficiently selecting informative prompts. However, its effectiveness critically depends on the selection of training data.

METHOD

Full abstract

Reinforcement learning (RL) finetuning has become a key technique for enhancing the reasoning abilities of large language models (LLMs). However, its effectiveness critically depends on the selection of training data. Recent advances underscore the importance of online prompt selection methods, which typically concentrate training on partially solved or moderately challenging examples under the current policy, thereby yielding more effective model updates. While significantly accelerating RL finetuning in terms of training steps, they also incur substantial computational overhead by requiring extensive LLM rollouts over large candidate batches to identify informative samples, an expense that can outweigh the finetuning process itself. To address this challenge, this work proposes Dynamics-Predictive Sampling (DPS), which online predicts and selects informative prompts by inferring their learning dynamics prior to costly rollouts. Specifically, we introduce a new perspective by modeling each prompt's solving progress during RL finetuning as a dynamical system, where the extent of solving is represented as the state and the transition is characterized by a hidden Markov model. Using historical rollout reward signals, we perform online Bayesian inference to estimate evolving state distributions, and the inference outcome provides a predictive prior for efficient prompt selection without rollout-intensive filtering. Empirical results across diverse reasoning tasks, including mathematics, planning, and visual geometry, demonstrate that DPS substantially reduces redundant rollouts, accelerates the training process, and achieves superior reasoning performance.

RESULT

ScienceToStartup currently rates this 7.0/10 on the public viability pass. Empirical results across diverse reasoning tasks, including mathematics, planning, and visual geometry, demonstrate that DPS substantially reduces redundant rollouts, accelerates the training process, and…

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

PainDynamics-Predictive Sampling accelerates RL finetuning for large reasoning models by efficiently selecting informative prompts.

Evidence0 refs | 0 sources | 17% coverage

Blockermissing authors

Analysis summary

Dynamics-Predictive Sampling accelerates RL finetuning for large reasoning models by efficiently selecting informative prompts.

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

ARXIV:2603.10887 · REINFORCEMENT LEARNING · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE

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

Dynamics-Predictive Sampling for Active RL Finetuning of Large Reasoning Models

arXiv

Dynamics-Predictive Sampling accelerates RL finetuning for large reasoning models by efficiently selecting informative prompts.

Blocked on Code›Score7.0Evidence unverified

Opportunity summary

Pain Dynamics-Predictive Sampling accelerates RL finetuning for large reasoning models by efficiently selecting informative prompts.

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

PainDynamics-Predictive Sampling accelerates RL finetuning for large reasoning models by efficiently selecting informative prompts.

Evidence0 refs | 0 sources | 17% coverage

Blockermissing authors

Analysis summary

Dynamics-Predictive Sampling accelerates RL finetuning for large reasoning models by efficiently selecting informative prompts.

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

Paper Pack

10.48550/arXiv.2603.10887

Dynamics-Predictive Sampling for Active RL Finetuning of Large Reasoning Models

Dynamics-Predictive Sampling accelerates RL finetuning for large reasoning models by efficiently selecting informative prompts.

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 fallback50 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
Dynamics-Predictive Sampling accelerates RL finetuning for large reasoning models by efficiently selecting informative prompts. However, its effectiveness critically depends on the selection of training data.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial
Evidencepartial
Reinforcement learning (RL) finetuning has become a key technique for enhancing the reasoning abilities of large language models (LLMs). However, its effectiveness critically depends on the selection of training data.
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. Empirical results across diverse reasoning tasks, including mathematics, planning, and visual geometry, demonstrate that DPS substantially reduces redundant rollouts, accelerates the training process, and achieves superior reasoning performance.
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

Dynamics-Predictive Sampling accelerates RL finetuning for large reasoning models by efficiently selecting informative prompts.

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 2603.10887 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(50)

Reference metadata pending (c2b18e7e7f617bf01193826a67adf3897b35ac65)

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Reference metadata pending (10feab31bb9e71a0f1094fb00c0554abfb992c4d)

Reference metadata pending (be1985ec9f0bce3b45c81e7b28b93acbaf7e20eb)

Reference metadata pending (1122b654f8b47c1aa9c04ff6bbe7561c798e2ad0)

Reference metadata pending (847d63914c51e3855ed7f13e74157daaa4a37d94)

Reference metadata pending (1f45c9d4d92c51a94c984eb4c2c6e027bbf78038)

Reference metadata pending (d85788857fd230169e17638631b96335368043ed)

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Reference metadata pending (aa06db9a5b5f8173502d6dd6fd4be96eda6a0f89)

Reference metadata pending (9b487024ccc5c575a6d72a597ad65f92cce79a04)

Reference metadata pending (d981ce332586e6a29f595cbdfa9347cf425e5cd0)

Reference metadata pending (de23d38bc2604dcf334dcc46aff217eb6bcd1fe1)

Reference metadata pending (16db56b5a57f2e675e5c4f60ca0fbd5764915a5e)

Reference metadata pending (cc769ab935638777a99f2a965b368c86d2cf52b4)

Reference metadata pending (dd4cfde3e135f799a9a71b4f57e13a29de89f7e3)

Reference metadata pending (a005bdec2395cccce42fad43a32aebb3d6284171)

Reference metadata pending (5f2f7305a467e01f76b1dbf5867fa34bd90edc19)

Reference metadata pending (446c3356b95a29dca4b908533c088ce63f2275cb)

Showing 20 of 50 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 WorkTrain at Moving Edge: Online-Verified Prompt Selection for Efficient RL Training of Large Reasoning Model

7.0

Prior WorkFrom $P(y|x)$ to $P(y)$: Investigating Reinforcement Learning in Pre-train Space

7.0

Extension

Builds On ThisGroup Distributionally Robust Optimization-Driven Reinforcement Learning for LLM Reasoning

5.0

Builds On ThisLearning from Synthetic Data Improves Multi-hop Reasoning

6.0

Commercially relevant

Higher ViabilityPrompting Policies for Multi-step Reasoning and Tool-Use in Black-box LLMs with Iterative Distillation of Experience

8.0

Conflicting

Competing ApproachSmall Generalizable Prompt Predictive Models Can Steer Efficient RL Post-Training of Large Reasoning Models

6.0

Competing ApproachADORA: Training Reasoning Models with Dynamic Advantage Estimation on Reinforcement Learning

5.0

Competing ApproachDynamical Priors as a Training Objective in Reinforcement Learning

4.0

Competing ApproachOffline Policy Optimization with Posterior Sampling

7.0

Competing ApproachLess Noise, More Voice: Reinforcement Learning for Reasoning via Instruction Purification

2.0

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: 2603.10887
Route: /paper/dynamics-predictive-sampling-for-active-rl-finetuning-of-large-reasoning-models
Active tab: read
Artifact: dynamics-predictive-sampling-for-active-rl-finetuning-of-large-reasoning-models

Available agents

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

API/MCP endpoints

REST paper pack API/api/v1/paper/dynamics-predictive-sampling-for-active-rl-finetuning-of-large-reasoning-models/paper-pack
REST build passport API/api/v1/paper/dynamics-predictive-sampling-for-active-rl-finetuning-of-large-reasoning-models/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/dynamics-predictive-sampling-for-active-rl-finetuning-of-large-reasoning-models

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

Dynamics-Predictive Sampling for Active RL Finetuning of Large Reasoning Models

Canonical ID dynamics-predictive-sampling-for-active-rl-finetuning-of-large-reasoning-models | Route /paper/dynamics-predictive-sampling-for-active-rl-finetuning-of-large-reasoning-models

REST example

curl https://sciencetostartup.com/api/v1/agent-handoff/paper/dynamics-predictive-sampling-for-active-rl-finetuning-of-large-reasoning-models

MCP example

{
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  "arguments": {
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  }
}

source_context

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  "paper_ref": "dynamics-predictive-sampling-for-active-rl-finetuning-of-large-reasoning-models",
  "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: Dynamics-Predictive Sampling for Active RL Finetuning of Large Reasoning Models

/buildability/dynamics-predictive-sampling-for-active-rl-finetuning-of-large-reasoning-models

Watchwatch

Subject: Dynamics-Predictive Sampling for Active RL Finetuning of Large Reasoning Models

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/dynamics-predictive-sampling-for-active-rl-finetuning-of-large-reasoning-models

Paper ref

dynamics-predictive-sampling-for-active-rl-finetuning-of-large-reasoning-models

arXiv id

2603.10887

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

2b5f6b641678b2beada1a39d21723f9baf6bc1bff01c84730d0bd0310f5bb7d6

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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Dynamics-Predictive Sampling for Active RL Finetuning of Large Reasoning Models

Dynamics-Predictive Sampling for Active RL Finetuning of Large Reasoning Models

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