Direct guidance of partial paths improves test-time accuracy reliably
A pruning method balances quality and diversity to pick the best partial thoughts without promising universal gains.
- Publication
- arXiv
- Stage
- Preprint
- What we read
- Summary of the paper
- Authors
- Mahdi Salmani, Adel Javanmard, Devansh Gupta, Meisam Razaviyayn
- Universities and research institutions
- Not yet supplied in verified metadata; the Brief does not guess.
What they did and found
The study looked at keeping partial ideas as a model tests many options. Guiding which ideas stay raises accuracy more reliably than only selecting the top score.
Why it matters
This helps teams decide how much effort to invest in exploring multiple ideas during inference, without assuming all workers benefit equally.
The study looked at keeping partial ideas as a model tests many options. Guiding which ideas stay raises accuracy more reliably than only selecting the top score.
In practice, you might use this in a decision tool by keeping a few prefixes that cover different approaches, then finish those paths to make a final choice.
What remains uncertain
Results may vary by domain and data quality; not guaranteed to apply to all tasks.
Original sources · 1
- Direct Pass@$k$ Optimization for Thought Pruning: Balancing Quality and Diversity in Test-Time Reasoning ↗arXiv · 2026-10-22
Check the original paper for its authors, methods, version and access terms.
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