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ARXIV:2604.01411 · LLM TRAINING · SUBMITTED 03 APR · 20:20 UTC · FRESHNESS STALE
ARXIV:2604.01411LLM TRAININGSUBMITTED 03 APR · 20:20 UTCFRESHNESS STALENicholas Roberts · Sungjun Cho · Zhiqi Gao · Tzu-Heng Huang · Albert Wu · Gabriel Orlanski · +4 at arXiv
Develops new scaling laws for LLM pretraining that optimize for end-to-end compute budgets, including inference costs, leading to overtrained models with improved performance.
Opportunity summary
Pain Develops new scaling laws for LLM pretraining that optimize for end-to-end compute budgets, including inference costs, leading to overtrained models with improved performance.
Evidence 0 refs | 0 sources | 17% coverage
Blocker Evidence unverified
Develops new scaling laws for LLM pretraining that optimize for end-to-end compute budgets, including inference costs, leading to overtrained models with improved performance. via repeated sampling, where inference cost grows with model size and…
Modern LLMs scale at test-time, e.g. via repeated sampling, where inference cost grows with model size and the number of samples.
ScienceToStartup currently rates this 3.0/10 on the public viability pass. We validate our results by pretraining heavily overtrained models in the optimal region that $T^2$ scaling forecasts, confirming their substantially stronger performance compared to…
LLM Training moved forward this cycle; last verified April 2026. Public score 3.0/10.
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Score3.0Analysis summary
Develops new scaling laws for LLM pretraining that optimize for end-to-end compute budgets, including inference costs, leading to overtrained models with improved performance.
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Paper Pack
10.48550/arXiv.2604.01411Develops new scaling laws for LLM pretraining that optimize for end-to-end compute budgets, including inference costs, leading to overtrained models with improved performance.
Abstract
Modern LLMs scale at test-time, e.g. via repeated sampling, where inference cost grows with model size and the number of samples. This creates a trade-off that pretraining scaling laws, such as Chinchilla, do not address. We present Train-to-Test ($T^2$) scaling laws that jointly optimize model size, training tokens, and number of inference samples under fixed end-to-end budgets. $T^2$ modernizes pretraining scaling laws with pass@$k$ modeling used for test-time scaling, then jointly optimizes pretraining and test-time decisions. Forecasts from $T^2$ are robust over distinct modeling approaches: measuring joint scaling effect on the task loss and modeling impact on task accuracy. Across eight downstream tasks, we find that when accounting for inference cost, optimal pretraining decisions shift radically into the overtraining regime, well-outside of the range of standard pretraining scaling suites. We validate our results by pretraining heavily overtrained models in the optimal region that $T^2$ scaling forecasts, confirming their substantially stronger performance compared to pretraining scaling alone. Finally, as frontier LLMs are post-trained, we show that our findings survive the post-training stage, making $T^2$ scaling meaningful in modern deployments.
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Extraction status
Derived fallbackRead summaries are estimated from adjacent metadata, not verified extraction rows.
Proof status
unverified0 refs; 0 sources; 17% coverage.
What was readable
Derived fallback: Estimated from adjacent evidence; not verified from source.
Viability
Time to MVP
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Preparing verified analysis
Dimensions overall score 3.0
PROBLEM
Develops new scaling laws for LLM pretraining that optimize for end-to-end compute budgets, including inference costs, leading to overtrained models with improved performance. via repeated sampling, where inference cost grows with model size and the number of samples.
METHOD
Modern LLMs scale at test-time, e.g. via repeated sampling, where inference cost grows with model size and the number of samples.
RESULT
ScienceToStartup currently rates this 3.0/10 on the public viability pass. We validate our results by pretraining heavily overtrained models in the optimal region that $T^2$ scaling forecasts, confirming their substantially stronger performance compared to pretraining scaling al...
WHY NOW
LLM Training moved forward this cycle; last verified April 2026. Public score 3.0/10.
Abstract-backed public claims while anchored extraction refreshes.
Develops new scaling laws for LLM pretraining that optimize for end-to-end compute budgets, including inference costs, leading to overtrained models with improved performance. via repeated sampling, where inference cost grows with model size and the number of samples.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Modern LLMs scale at test-time, e.g. via repeated sampling, where inference cost grows with model size and the number of samples.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 3.0/10 on the public viability pass. We validate our results by pretraining heavily overtrained models in the optimal region that $T^2$ scaling forecasts, confirming their substantially stronger performance compared to pretraining scaling alone.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
LLM Training moved forward this cycle; last verified April 2026. Public score 3.0/10.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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Concepts
Methods
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Develops new scaling laws for LLM pretraining that optimize for end-to-end compute budgets, including inference costs, leading to overtrained models with improved performance.
Segment
LLM Training
Adoption evidence
No public code link in the paper record yet
Commercial read
3.0/10 public viability
Direct
Adjacent
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CITED BY
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Build Passport
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status
missing
reason
passport_row_missing
proof status
unverified
cost/budget
No verified cost estimate
confidence low
next verification path
Build brief missing until Build Passport data exists.
Source missing: Build Passport payload.
Experiment plan missing until prototype path is available.
No prototype path attached.
Validation checklist missing until required assets, cost, and regulatory flags are verified.
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Evidence coverage
OpportunityKernel evidence_receipt
0 refs / 0 sources / 17% coverage
stale
Verify missing sources before using this as buyer proof. verified:false
Build readiness
BuildPassport EvidenceState
passport absent
stale
Run Proof Lab or inspect typed missing state. verified:false
Artifact maturity
GitHub and Hugging Face maturity payloads
No public artifact surface observed
stale
Open source artifacts or mark the gap as missing. verified:false
Technical feasibility
partial
Current read
Runnable path is not fully verified.
Evidence
No Build Passport payload attached.
Gaps
Next test
Run minimal reproduction from the Build Passport prototype path.
Market urgency
missing
Current read
Buyer urgency is not verified from source.
Evidence
0 references, 0 sources, 17% evidence coverage.
Gaps
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Collect buyer interview, deployment evidence, or cited demand signal.
Buyer clarity
missing
Current read
No budget owner is verified for this paper.
Evidence
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Gaps
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Map target operator, economic buyer, and procurement trigger.
Defensibility
missing
Current read
Defensibility signals are missing.
Evidence
No defensibility receipt attached.
Gaps
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Refresh defensibility bars with source receipts.
Integration burden
missing
Current read
No public implementation surface observed.
Evidence
No GitHub or Hugging Face payload attached.
Gaps
Next test
Write integration checklist from prototype path and target workflow.
Capital intensity
missing
Current read
No observed cost estimate is verified.
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Run cost passport or mark the cost field not applicable.
Regulatory load
missing
Current read
No regulatory classification is attached.
Evidence
Build Passport ledger does not include regulatory flags.
Gaps
Next test
Classify regulatory flags before commercialization planning.
No named scientific founder assigned.
Paper authors are not treated as operators without consent.
People
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Gaps
Next verification path
Prototype owner missing.
Build Passport does not name an implementer.
People
No named person assigned.
Gaps
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Operator workflow not sourced.
No buyer or workflow interview attached.
People
No named person assigned.
Gaps
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No CRM or outreach source attached.
People
No named person assigned.
Gaps
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Regulatory need unclassified.
No clinical or regulatory source attached.
People
No named person assigned.
Gaps
Next verification path
ARTIFACTS
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DEFENSIBILITY
Defensibility and confidence evidence pending.
WATCHTOWER
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FORESIGHT
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OPPORTUNITYKERNEL CHANGES SINCE LAST VIEW
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COMPETITIVE LANDSCAPE UPDATES
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RELATED PAPER UPDATES
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SIGNAL CANVAS HISTORY AND DELTAS
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TIMELINE
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BUZZ
Buzz trend pending.