OpenDeepThink: Parallel Reasoning via Bradley-Terry Aggregation

TL;DR

OpenDeepThink introduces a population-based framework for LLM reasoning that uses pairwise comparisons and evolutionary mutation to improve answer quality without ground-truth verification.

Contribution

It proposes a novel parallel reasoning method using Bradley-Terry comparisons and mutation, enhancing LLM reasoning performance across models and domains.

Findings

Raises Gemini 3.1 Pro's Elo by +405 points in 8 rounds

Transfers across models without retuning

Gains concentrated in verifiable domains

Abstract

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.