VibeThinker 3B GGUF VibeThinker 3B is a 3 billion parameter reasoning focused language model developed by WeiboAI, built on top of Qwen2.5 Coder 3B and trained using the Spectrum to Signal Principle (SSP) post training pipeline, which combines curriculum based two stage SFT, multi domain reinforcement learning via MaxEnt Guided Policy Optimization (MGPO), offline self distillation, and instruct RL to systematically develop strong verifiable reasoning capabilities across mathematics, coding, and STEM tasks. Motivated by the Parametric Compression Coverage Hypothesis — which posits that verifiable reasoning is a highly compressible, parameter dense capability that compact models can carry near frontier performance in — VibeThinker 3B achieves remarkable results for its size, scoring 76.4 on IMO AnswerBench (improving to 80.6 with Claim Level Reliability Assessment test time scaling), competing with models like DeepSeek V3.2 (671B) and Kimi K2.5 (1T), while also achieving a 96.1% acceptance rate (123/128 submissions) on recent unseen LeetCode weekly and biweekly contests from April–May 2026, and reaching the performance range of top tier frontier reasoning systems including Qwen3.6 Pl…
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