Many teams need strong reasoning abilities for math, coding and STEM tasks but cannot afford the memory and cost of huge models. Running a 600B‑parameter network requires multiple GPUs, expensive infrastructure and slow inference, which blocks rapid prototyping and limits access for smaller labs or startups. The core problem is therefore: how to get top‑tier verifiable reasoning performance while staying within a single‑GPU budget and keeping latency low.
VibeThinker-3B solves this by delivering 3‑billion‑parameter performance that rivals models hundreds of times larger. Built on Qwen2.5‑Coder‑3B and refined with the Spectrum‑to‑Signal post‑training pipeline, it uses supervised fine‑tuning to create a broad space of valid reasoning paths, then reinforcement learning to amplify the correct ones. The model skips progressive context expansion, keeping a stable 64K window that prevents long‑reasoning degradation at this scale. A Long2Short stage in math RL rewards shorter correct trajectories, cutting redundant tokens without losing accuracy. Offline self‑distillation merges RL checkpoints into a single student, and an final instruct‑RL stage preserves instruction following.
Deployment is straightforward: install transformers>=4.54.0, then use vLLM==0.10.1 or SGLang>=0.4.9.post6 for faster inference. The BF16 weights are about 6 GB, fitting comfortably on a single consumer‑grade GPU. For tasks where answers can be verified, the Claim‑Level Reliability Assessment (CLR) test‑time scaling adds no parameters. It generates multiple trajectories, extracts key claims, validates them internally, and selects the answer with the highest reliability score—boosting AIME26 from 94.3 to 97.1 and BruMO25 from 93.8 to 99.2.
In practice, a developer can download the model, load it with the suggested stack, run inference on math or code problems, and optionally enable CLR for extra accuracy. This approach gives high‑quality reasoning on a modest hardware footprint, reduces cloud costs, and speeds up iteration cycles.
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