Human Feedback Types
provisional (inferred)None explicit
No explicit feedback protocol extracted.
"Scaling test-time compute has emerged as an effective strategy for improving the performance of large language models."
HFEPX · Eval paper review
Strategic Scaling of Test-Time Compute: A Bandit Learning Approach
Bowen Zuo, Yinglun Zhu
Published
Jun 15, 2025
Citations
0
Trust level
Provisional
Usefulness score
Unavailable
Extraction confidence
0% (Provisional)
Derived from abstract and metadata only.
Signals refreshed
Apr 23, 2026
Should you rely on this paper?
Signal extraction is still processing. This page currently shows metadata-first guidance until structured protocol fields are ready.
This page is a lightweight research summary built from the abstract and metadata while deeper extraction catches up.
Abstract-only analysis, low confidence
All signals on this page are inferred from the abstract only and may be inaccurate. Do not use this page as a primary protocol reference.
- This page is still relying on abstract and metadata signals, not a fuller protocol read.
Best use
Background context only
Use if you need
A provisional background reference while structured extraction finishes.
What to verify
Read the full paper before copying any benchmark, metric, or protocol choices.
Main weakness
This page is still relying on abstract and metadata signals, not a fuller protocol read.
Human feedback signal
Not explicit
Not explicit in abstract metadata
Evaluation signal
Weak or implicit
Validate from full paper
Usefulness for eval research
Unavailable
Provisional (processing)
Eval-fit score is unavailable until extraction completes.
If you are doing eval pipeline work, start here
Abstract
Scaling test-time compute has emerged as an effective strategy for improving the performance of large language models. However, existing methods typically allocate compute uniformly across all queries, overlooking variation in query difficulty. To address this inefficiency, we formulate test-time compute allocation as a novel bandit learning problem and propose adaptive algorithms that estimate query difficulty on the fly and allocate compute accordingly. Compared to uniform allocation, our algorithms allocate more compute to challenging queries while maintaining accuracy on easier ones. Among challenging queries, our algorithms further learn to prioritize solvable instances, effectively reducing excessive computing on unsolvable queries. We theoretically prove that our algorithms achieve better compute efficiency than uniform allocation and empirically validate their effectiveness on math and code benchmarks. Specifically, our algorithms achieve up to an 11.10% performance improvement (15.04% relative) on the MATH-500 dataset, up to 10.82% (14.44% relative) on the AIME25 dataset, and up to an 11.23% performance improvement (15.29% relative) on the LiveCodeBench dataset.
What we could verify
These are the protocol signals we could actually recover from the available paper metadata. Use them to decide whether this paper is worth deeper reading.
Evaluation Modes
provisional (inferred)Automatic metrics
Includes extracted eval setup.
"Scaling test-time compute has emerged as an effective strategy for improving the performance of large language models."
Quality Controls
provisional (inferred)Not reported
No explicit QC controls found.
"Scaling test-time compute has emerged as an effective strategy for improving the performance of large language models."
Benchmarks / Datasets
provisional (inferred)LiveCodeBench, MATH
Useful for quick benchmark comparison.
"We theoretically prove that our algorithms achieve better compute efficiency than uniform allocation and empirically validate their effectiveness on math and code benchmarks."
Reported Metrics
provisional (inferred)Accuracy
Useful for evaluation criteria comparison.
"Compared to uniform allocation, our algorithms allocate more compute to challenging queries while maintaining accuracy on easier ones."
Rater Population
provisional (inferred)Unknown
Rater source not explicitly reported.
"Scaling test-time compute has emerged as an effective strategy for improving the performance of large language models."
Human feedback details
This page is using abstract-level cues only right now. Treat the signals below as provisional.
- Potential human-data signal: No explicit human-data keywords detected.
- Potential benchmark anchors: LiveCodeBench, MATH
- Abstract highlights: 3 key sentence(s) extracted below.
Evaluation details
Evaluation fields are inferred from the abstract only.
- Potential evaluation modes: Automatic metrics
- Potential metric signals: Accuracy
- Confidence: Provisional (metadata-only fallback).
Research brief
Metadata summary
Scaling test-time compute has emerged as an effective strategy for improving the performance of large language models.
Based on abstract + metadata only. Check the source paper before making high-confidence protocol decisions.
Key takeaways
- Scaling test-time compute has emerged as an effective strategy for improving the performance of large language models.
- However, existing methods typically allocate compute uniformly across all queries, overlooking variation in query difficulty.
- To address this inefficiency, we formulate test-time compute allocation as a novel bandit learning problem and propose adaptive algorithms that estimate query difficulty on the fly and allocate compute accordingly.
Researcher actions
- Compare this paper against others mentioning LiveCodeBench and MATH.
- Validate inferred eval signals (Automatic metrics) against the full paper.
- Use related-paper links to find stronger protocol-specific references.
Caveats
- Generated from abstract + metadata only; no PDF parsing.
- Signals below are heuristic and may miss details reported outside the abstract.