# Direct Pass@$k$ Optimization for Thought Pruning: Balancing Quality and Diversity in Test-Time Reasoning

Source: [arXiv](https://arxiv.org/abs/2610.31667v1)  
Feed7 permalink: https://feed7.dev/p/2610-31667v1-1jz3l6f  
Published: 2026-10-22T17:59:48.000Z  
Trust: Needs Review (needs_review)

## Why Included

A pruning objective selects reasoning branches for their combined pass@k, retaining promising but nonredundant paths so test-time compute is spent on a stronger candidate pool.

## Source Summary

The method scores a pool of partial solutions by the chance that at least one continuation will be correct. It was tested on **eight open-weight reasoning models** and outperformed quality-only and diversity-only pruning.

## Practical Implication

For agent workloads that sample several reasoning paths, optimize the surviving set rather than ranking branches independently. **Direct pass@k optimization** makes both predicted quality and overlap between candidates part of one selection criterion.

## Agent-Ready Context

The method scores a pool of partial solutions by the chance that at least one continuation will be correct. It was tested on **eight open-weight reasoning models** and outperformed quality-only and diversity-only pruning.

For agent workloads that sample several reasoning paths, optimize the surviving set rather than ranking branches independently. **Direct pass@k optimization** makes both predicted quality and overlap between candidates part of one selection criterion.

The abstract provides no effect sizes or compute-cost comparison. Gains were strongest with a **moderate number of survivors**, so applicability to very small or very large candidate pools remains unclear.

## Connected Context

Feed7 judgment across 898 accumulated Signals:

This narrows test-time branch pruning to a set-level objective: retain candidates according to the probability that at least one continuation succeeds, rather than treating quality and diversity as separate heuristics. It supports evaluating reasoning as a multi-path inference system, but absent effect sizes and compute comparisons leave the practical advantage and useful pool sizes unresolved.

- [Test-Time Scaling in Reasoning LLMs: Inference Regimes, Evaluation, and Reproducibility](https://feed7.dev/p/2608-04001v1-1gj91hk) — Direct pass@k pruning instantiates one specific multi-path inference regime, reinforcing the need to report survivor counts, sampling and pruning protocol, compute, and uncertainty rather than attributing gains to the base model alone.
- [Legibility is Not Interpretability: Comparing Judged and Actual Importance in Chain-Of-Thought Reasoning](https://feed7.dev/p/2609-04194v1-0milybu) — The set-level success objective avoids relying solely on readable or independently judged reasoning steps, consistent with evidence that apparent step importance need not reflect causal contribution.

## Context Map

- Layer: benchmark
- Domains: research
- Topics: reasoning, agent-evals

## Uncertainty

- The abstract provides no effect sizes or compute-cost comparison. Gains were strongest with a **moderate number of survivors**, so applicability to very small or very large candidate pools remains unclear.

## Agent Instruction

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