ARXIV:2602.03690 · OPTIMIZATION FOR DECISION-MAKING · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE

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

LLM-Inspired Pretrain-Then-Finetune for Small-Data, Large-Scale Optimization

arXiv

Develop a Transformer-based solution for optimizing decision-making across large-scale portfolios using limited data through a pretrain-then-finetune pipeline.

Blocked on Code›Score3.0Evidence unverified

Opportunity summary

Pain Develop a Transformer-based solution for optimizing decision-making across large-scale portfolios using limited data through a pretrain-then-finetune pipeline.

Evidence 0 refs | 0 sources | 17% coverage

Blocker Evidence unverified

Open Build Read PDF Signal Canvas Track

PROBLEM

Develop a Transformer-based solution for optimizing decision-making across large-scale portfolios using limited data through a pretrain-then-finetune pipeline. Inspired by the success of large language models (LLMs), we propose a pretrain-then-finetune approach built on a…

METHOD

Full abstract

We consider small-data, large-scale decision problems in which a firm must make many operational decisions simultaneously (e.g., across a large product portfolio) while observing only a few, potentially noisy, data points per instance. Inspired by the success of large language models (LLMs), we propose a pretrain-then-finetune approach built on a designed Transformer model to address this challenge. The model is first pretrained on large-scale, domain-informed synthetic data that encode managerial knowledge and structural features of the decision environment, and is then fine-tuned on real observations. This new pipeline offers two complementary advantages: pretraining injects domain knowledge into the learning process and enables the training of high-capacity models using abundant synthetic data, while finetuning adapts the pretrained model to the operational environment and improves alignment with the true data-generating regime. While we have leveraged the Transformer's state-of-the-art representational capacity, particularly its attention mechanism, to efficiently extract cross-task structure, our approach is not an off-the-shelf application. Instead, it relies on problem-specific architectural design and a tailored training procedure to match the decision setting. Theoretically, we develop the first comprehensive error analysis regarding Transformer learning in relevant contexts, establishing nonasymptotic guarantees that validate the method's effectiveness. Critically, our analysis reveals how pretraining and fine-tuning jointly determine performance, with the dominant contribution governed by whichever is more favorable. In particular, finetuning exhibits an economies-of-scale effect, whereby transfer learning becomes increasingly effective as the number of instances grows.

RESULT

ScienceToStartup currently rates this 3.0/10 on the public viability pass. This new pipeline offers two complementary advantages: pretraining injects domain knowledge into the learning process and enables the training of high-capacity models using abundant…

WHY NOW

Optimization for Decision-Making moved forward this cycle; last verified April 2026. Public score 3.0/10.

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

Opportunity summary

Score3.0

PainDevelop a Transformer-based solution for optimizing decision-making across large-scale portfolios using limited data through a pretrain-then-finetune pipeline.

Evidence0 refs | 0 sources | 17% coverage

Blockermissing authors

Analysis summary

Develop a Transformer-based solution for optimizing decision-making across large-scale portfolios using limited data through a pretrain-then-finetune pipeline.

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

ARXIV:2602.03690 · OPTIMIZATION FOR DECISION-MAKING · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE

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

LLM-Inspired Pretrain-Then-Finetune for Small-Data, Large-Scale Optimization

arXiv

Develop a Transformer-based solution for optimizing decision-making across large-scale portfolios using limited data through a pretrain-then-finetune pipeline.

Blocked on Code›Score3.0Evidence unverified

Opportunity summary

Pain Develop a Transformer-based solution for optimizing decision-making across large-scale portfolios using limited data through a pretrain-then-finetune pipeline.

Evidence 0 refs | 0 sources | 17% coverage

Blocker Evidence unverified

Open Build Read PDF Signal Canvas Track

PROBLEM

METHOD

Full abstract

RESULT

WHY NOW

Optimization for Decision-Making moved forward this cycle; last verified April 2026. Public score 3.0/10.

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

Opportunity summary

Score3.0

PainDevelop a Transformer-based solution for optimizing decision-making across large-scale portfolios using limited data through a pretrain-then-finetune pipeline.

Evidence0 refs | 0 sources | 17% coverage

Blockermissing authors

Analysis summary

Develop a Transformer-based solution for optimizing decision-making across large-scale portfolios using limited data through a pretrain-then-finetune pipeline.

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

Paper Pack

10.48550/arXiv.2602.03690

LLM-Inspired Pretrain-Then-Finetune for Small-Data, Large-Scale Optimization

Develop a Transformer-based solution for optimizing decision-making across large-scale portfolios using limited data through a pretrain-then-finetune pipeline.

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 fallback47 indexednot indexed

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

Viability

3.0

Time to MVP

MVP estimate missing

Commercial

No commercial flags on file

Export

Preparing verified analysis

lens / founder

PROBLEM

METHOD

RESULT

WHY NOW

Optimization for Decision-Making moved forward this cycle; last verified April 2026. Public score 3.0/10.

Claim map

Abstract-backed public claims while anchored extraction refreshes.

Strong 0Mixed 0Weak 4

Evidencepartial
Develop a Transformer-based solution for optimizing decision-making across large-scale portfolios using limited data through a pretrain-then-finetune pipeline. Inspired by the success of large language models (LLMs), we propose a pretrain-then-finetune approach built on a designed Transformer model to address this challenge.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial
Evidencepartial
We consider small-data, large-scale decision problems in which a firm must make many operational decisions simultaneously (e.g., across a large product portfolio) while observing only a few, potentially noisy, data points per instance. Inspired by the success of large language models (LLMs), we propose a pretrain-then-finetune approach built on a designed Transformer model to address this challenge.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial
Evidencepartial
ScienceToStartup currently rates this 3.0/10 on the public viability pass. This new pipeline offers two complementary advantages: pretraining injects domain knowledge into the learning process and enables the training of high-capacity models using abundant synthetic data, while finetuning adapts the pretrained model to the operational environment and improves alignment with the true data-generating regime.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial
Evidencepartial
Optimization for Decision-Making moved forward this cycle; last verified April 2026. Public score 3.0/10.
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

Optimization for Decision-Making

Competitors

not indexed

Competitive landscape

Develop a Transformer-based solution for optimizing decision-making across large-scale portfolios using limited data through a pretrain-then-finetune pipeline.

Segment

Optimization for Decision-Making

Adoption evidence

No public code link in the paper record yet

Commercial read

3.0/10 public viability

Direct

not classified

Adjacent

not classified

Substitute

not classified

Unknown

not classified

Buzz

No indexed public discussion is attached to 2602.03690 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(47)

Reference metadata pending (4f9d7d0b2037c3f71dcb636e6378a79c4ad88f54)

Reference metadata pending (e9cd6c36a58520698248a08cb1db8c4a974f2557)

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Reference metadata pending (af498bbf90140b1d48ed48b753ba633a96a5a7dc)

Reference metadata pending (de15adff32896fa645ab46392ef0a84e23aa66a2)

Reference metadata pending (cc7f37a261eaa60113716b8e969da6b57d206da6)

Reference metadata pending (481c745eaea4a8e58208c6b138390ba49706a88c)

Reference metadata pending (12065f7e05902cfd4532564af8438316fb804ff5)

Reference metadata pending (b673a9e909cbbc42a6a917b9c615cf512750f621)

Reference metadata pending (47e2e9671bb407263c72da95831814b4c7f3d102)

Reference metadata pending (8cacc202b5f49da910dfd714b877c4d05ff516d7)

Reference metadata pending (c14222284603b0026e696814034d5844f38ba121)

Reference metadata pending (d3a6e9790ef423497e855bdacbbc357e962ddd71)

Reference metadata pending (b259dbadd2bc03a45b56b4151b9e91f4becdbff0)

Reference metadata pending (525d93a382f6e7873b5d8a2e0713eb3dff7fb250)

Reference metadata pending (9f5b82d9915d0752957602224c5056be7e749c83)

Reference metadata pending (8c7590f9e70b14a89bd045e580c4ac6689caf721)

Reference metadata pending (dc32a984b651256a8ec282be52310e6bd33d9815)

Showing 20 of 47 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 WorkTFGN: Task-Free, Replay-Free Continual Pre-Training Without Catastrophic Forgetting at LLM Scale

3.0

Extension

Builds On ThisLarge Language Model Post-Training: A Unified View of Off-Policy and On-Policy Learning

1.0

Commercially relevant

Higher ViabilityTheoretical Perspectives on Data Quality and Synergistic Effects in Pre- and Post-Training Reasoning Models

5.0

Higher ViabilityLLM Pretraining Shapes a Generalizable Manifold: Insights into Cross-Modal Transfer to Time Series

4.0

Higher ViabilityThe Finetuner's Fallacy: When to Pretrain with Your Finetuning Data

5.0

Higher ViabilityGrow, Don't Overwrite: Fine-tuning Without Forgetting

7.0

Higher ViabilityTuneShift-KD: Knowledge Distillation and Transfer for Fine-tuned Models

7.0

Higher ViabilityFT-Dojo: Towards Autonomous LLM Fine-Tuning with Language Agents

8.0

Higher ViabilityPreFT: Prefill-only finetuning for efficient inference

6.0

Higher ViabilityTransformers in the Dark: Navigating Unknown Search Spaces via Bandit Feedback

7.0

Conflicting

none indexed

Owned Distribution

Subscribe to the weekly brief

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: 2602.03690
Route: /paper/llm-inspired-pretrain-then-finetune-for-small-data-large-scale-optimization
Active tab: read
Artifact: llm-inspired-pretrain-then-finetune-for-small-data-large-scale-optimization

Available agents

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

API/MCP endpoints

REST paper pack API/api/v1/paper/llm-inspired-pretrain-then-finetune-for-small-data-large-scale-optimization/paper-pack
REST build passport API/api/v1/paper/llm-inspired-pretrain-then-finetune-for-small-data-large-scale-optimization/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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  "paper_id": "a975017d-3597-4401-8dee-4ce3ea0386f3",
  "arxiv_id": "2602.03690",
  "canonical_route": "/paper/llm-inspired-pretrain-then-finetune-for-small-data-large-scale-optimization",
  "active_tab": "synced from current hash by the drawer client",
  "selected_artifact": "llm-inspired-pretrain-then-finetune-for-small-data-large-scale-optimization",
  "endpoints": {
    "paper_pack": "/api/v1/paper/llm-inspired-pretrain-then-finetune-for-small-data-large-scale-optimization/paper-pack",
    "build_passport": "/api/v1/paper/llm-inspired-pretrain-then-finetune-for-small-data-large-scale-optimization/build-passport",
    "mcp_resource": "sciencetostartup://surfaces/paper-workspace"
  }
}

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/llm-inspired-pretrain-then-finetune-for-small-data-large-scale-optimization

stale

Proof freshness: stale
Proof status: unverified
Display score: 3/10
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

LLM-Inspired Pretrain-Then-Finetune for Small-Data, Large-Scale Optimization

Canonical ID llm-inspired-pretrain-then-finetune-for-small-data-large-scale-optimization | Route /paper/llm-inspired-pretrain-then-finetune-for-small-data-large-scale-optimization

REST example

curl https://sciencetostartup.com/api/v1/agent-handoff/paper/llm-inspired-pretrain-then-finetune-for-small-data-large-scale-optimization

MCP example

{
  "tool": "get_paper",
  "arguments": {
    "arxiv_id": "2602.03690"
  }
}

source_context

{
  "surface": "paper",
  "mode": "paper",
  "query": "LLM-Inspired Pretrain-Then-Finetune for Small-Data, Large-Scale Optimization",
  "normalized_query": "2602.03690",
  "route": "/paper/llm-inspired-pretrain-then-finetune-for-small-data-large-scale-optimization",
  "paper_ref": "llm-inspired-pretrain-then-finetune-for-small-data-large-scale-optimization",
  "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

Not build-ready: LLM-Inspired Pretrain-Then-Finetune for Small-Data, Large-Scale Optimization

/buildability/llm-inspired-pretrain-then-finetune-for-small-data-large-scale-optimization

Ignoreblocked

Subject: LLM-Inspired Pretrain-Then-Finetune for Small-Data, Large-Scale Optimization

Verdict

Ignore

Verdict is Ignore because current viability and proof state do not clear the buildability gate.

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/llm-inspired-pretrain-then-finetune-for-small-data-large-scale-optimization

Paper ref

llm-inspired-pretrain-then-finetune-for-small-data-large-scale-optimization

arXiv id

2602.03690

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

dc74f4bfdf58f4939d7d5d70b75e899492f23374f0efc7f1d3e17ecd05b5eff9

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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LLM-Inspired Pretrain-Then-Finetune for Small-Data, Large-Scale Optimization

LLM-Inspired Pretrain-Then-Finetune for Small-Data, Large-Scale Optimization

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Claim map

Constellation map

Competitive landscape

Buzz

PDF

References(47)

Related Papers

Subscribe to the weekly brief

Build artifacts

Brief

Experiment plan

Validation checklist

Scientific founder

Translational engineer

Domain operator

GTM lead

Regulatory/clinical advisor

Timeline