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How Much Can Language Models Gain from Test-Time Computation?
arXiv cs.LG — Machine Learning
Factual evidence
What the source reports
Researchers introduced SELF-POT, a benchmark evaluating test-time compute scaling efficiency across math, code, and agentic workflows.
Inspect the evidence
- Inclusion basis
- Enterprise AI
- Publisher and source type
- arXiv cs.LG — Machine Learning · RESEARCH
- Published by source
- 3 October 2026
- Collected by OneBench
- 4 Oct 2026, 03:02 UK
- Original headline
- How Much Can Language Models Gain from Test-Time Computation? ↗
Stored source excerpt
arXiv:2610.01110v1 Announce Type: new Abstract: How much can test-time computation improve a language model, and at what cost? Test-time scaling is widely proposed as a…
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