OpenDeepThink: Parallel Reasoning via Bradley-Terry Aggregation
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arXiv
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| Format: | Preprint |
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2026
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| _version_ | 1866916019973390336 |
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| author | Zhou, Shang Chai, Wenhao Liu, Kaiyuan Mao, Huanzhi Mang, Qiuyang Shang, Jingbo |
| author_facet | Zhou, Shang Chai, Wenhao Liu, Kaiyuan Mao, Huanzhi Mang, Qiuyang Shang, Jingbo |
| contents | Test-time compute scaling is a primary axis for improving LLM reasoning. Existing methods primarily scale depth by extending a single reasoning trace. Scaling breadth by sampling multiple candidates in parallel is straightforward, but introduces a selection bottleneck: choosing the best candidate without a ground-truth verifier, since pointwise LLM judging is noisy and biased. To address this, we introduce OpenDeepThink, a population-based test-time compute framework that selects via pairwise Bradley-Terry comparison. Each generation, the LLM judges random pairs of candidates and aggregates votes via Bradley-Terry into a global ranking; top-ranked candidates are preserved and the top three quarters are mutated using the natural-language critiques produced during comparison; the bottom quarter is discarded. OpenDeepThink raises Gemini 3.1 Pro's effective Codeforces Elo by +405 points in eight sequential LLM-call rounds (~27 minutes wall-clock). The pipeline transfers across weaker and stronger models without retuning, and on the multi-domain HLE benchmark, gains appear concentrated in objectively verifiable domains and reverse in subjective ones. We release CF-73, a curated set of 73 expert-rated Codeforces problems with International Grandmaster annotation and 99% local-evaluation agreement against the official verdict. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_15177 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | OpenDeepThink: Parallel Reasoning via Bradley-Terry Aggregation Zhou, Shang Chai, Wenhao Liu, Kaiyuan Mao, Huanzhi Mang, Qiuyang Shang, Jingbo Artificial Intelligence Test-time compute scaling is a primary axis for improving LLM reasoning. Existing methods primarily scale depth by extending a single reasoning trace. Scaling breadth by sampling multiple candidates in parallel is straightforward, but introduces a selection bottleneck: choosing the best candidate without a ground-truth verifier, since pointwise LLM judging is noisy and biased. To address this, we introduce OpenDeepThink, a population-based test-time compute framework that selects via pairwise Bradley-Terry comparison. Each generation, the LLM judges random pairs of candidates and aggregates votes via Bradley-Terry into a global ranking; top-ranked candidates are preserved and the top three quarters are mutated using the natural-language critiques produced during comparison; the bottom quarter is discarded. OpenDeepThink raises Gemini 3.1 Pro's effective Codeforces Elo by +405 points in eight sequential LLM-call rounds (~27 minutes wall-clock). The pipeline transfers across weaker and stronger models without retuning, and on the multi-domain HLE benchmark, gains appear concentrated in objectively verifiable domains and reverse in subjective ones. We release CF-73, a curated set of 73 expert-rated Codeforces problems with International Grandmaster annotation and 99% local-evaluation agreement against the official verdict. |
| title | OpenDeepThink: Parallel Reasoning via Bradley-Terry Aggregation |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2605.15177 |