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When Independent Sampling Outperforms Agentic Reasoning
Yihe Dong, B · 2026-05-12 · via cs.LG updates on arXiv.org

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Abstract:We study how to allocate inference-time compute for competitive programming under fixed budgets. Evaluating 216 Codeforces problems across Divisions 1-3, we compare agent-based reasoning with repeated independent sampling (k-shot) as a function of both cost and number of model calls. Across models and difficulty levels, k-shot consistently achieves a better accuracy-cost and accuracy-query tradeoff. This gap persists despite prompt caching in agent frameworks, indicating lower per-call effectiveness. Our results show that, for self-contained algorithmic tasks, independent exploration can outperform deeper agentic reasoning under realistic resource constraints. We also provide a budget-allocation analysis when the inference budget is fixed, and prove that a cost-optimal solver minimizes the principled metric log failure likelihood per dollar.
Subjects: Machine Learning (cs.LG)
Cite as: arXiv:2605.08478 [cs.LG]
  (or arXiv:2605.08478v1 [cs.LG] for this version)
  https://doi.org/10.48550/arXiv.2605.08478

arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Boris Shigida [view email]
[v1] Fri, 8 May 2026 20:53:51 UTC (749 KB)