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Test-Time-Compute-Scaling-Risk-Dataset

Simulated true-accuracy-vs-search-width curves for 180 configurations across 5 reasoning tasks and 6 test-time-compute methods (weak/strong-verifier best-of-N, beam search, self-consistency, PRM-guided search, verification-based finetuning), grounded in 2023-2026 research on search-time reward overoptimization. Forward-looking task predicts overoptimization at a large search budget from a cheap small-budget check.

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updated 12 Aug 2026

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COVERAGE India

LICENSE Attribution 4.0 International (CC BY- 4.0)

FORMAT Synthetic

TYPE Structured

SECTOR Science, Technology and Research

UPDATES NA

About Dataset

Simulates a search-time instance of Goodhart's Law: the verifier/proxy score always rises with more search, but true accuracy rises then declines past a critical width for most methods. 180 configs, 6 methods. Ten sources (2021-2026) in test_time_compute_theory.py, including Gao et al.'s reward-overoptimization scaling laws, a formal best-of-N analysis, an ICML 2025 spotlight paper, and a 2026 paper extending the finding to beam search via extreme value theory.

Dataset Metadata

License

Attribution 4.0 International (CC BY- 4.0)

Sector

Science, Technology and Research

Author

Jai Mali

Source Organisation

Symbiosis Skills and Professional University

Uploaded By

Data Quality Score (Beta)

4.5

Dataset Type

Structured

Data Type

Synthetic

Task Category

N.A.

Time Granularity

NA

Frequency

NA

Geographic Granularity

N.A.

Primary Key / Indicator

N.A.

Data Collection Method

N.A.

Language

N.A.

Year Range

N.A.

Date & Time

09/08/26 17:19:34

Visibility

Open

Hosted / Redirected

Hosted

Tags

Large Language Model

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