Bespoke-Stratos + Sky-T1: The Vicuna+Alpaca moment for reasoning

**Reasoning Distillation** has emerged as a key technique, with Berkeley/USC researchers releasing **Sky-T1-32B-Preview**, a finetuned model of **Qwen 2.5 32B** using 17k reasoning traces for just **$450**, matching benchmarks of **o1-preview**. **DeepSeek** introduced **R1**, a model surpassing **o1-preview** and enabling distillation to smaller models like a 1.5B Qwen to match **gpt-4o** and **claude-3-sonnet** levels. **Bespoke Labs** further distilled **R1** on Qwen, outperforming **o1-preview** with fewer samples. This progress suggests that *"SFT is all you need"* for reasoning without major architecture changes. Additionally, **DeepSeek-R1** uses pure reinforcement learning with supervised finetuning to accelerate convergence and shows strong reasoning and multimodal capabilities. **Google's Gemini 2.0 Flash Thinking** model boasts a **1 million token context window**, code execution, and excels in math, science, and multimodal reasoning. Critiques highlight challenges in model repeatability, behavioral self-awareness, and RLHF limitations in reasoning robustness.