VibeThinker-3B achieves AIME26 97.1 and LiveCodeBench 80.2 Pass@1 through curriculum fine-tuning and offline self-distillation, collapsing the parameter-to-performance curve for verifiable reasoning tasks.
Summary
Compact models can now handle hard reasoning workloads without deploying billion-parameter flagships, directly reducing inference latency and cost for math/code completion features. This reshapes the frontier-vs-efficiency tradeoff for reasoning-heavy applications.
Why it matters
Compact models can now handle hard reasoning workloads without deploying billion-parameter flagships, directly reducing inference latency and cost for math/code completion features. This reshapes the frontier-vs-efficiency tradeoff for reasoning-heavy applications.
Implementation verdict
Replaces deployment logic that mandates large models for AIME/LeetCode-tier tasks. Requires integration with test-time scaling (claim-level) and curriculum-aware fine-tuning pipelines. Worth immediate evaluation if you're running reasoning inference at scale; benchmark against your own AIME/LiveCodeBench subsets before committing.
Sources
Dev Signal
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