All tags
Topic: "supervised-finetuning"
Bespoke-Stratos + Sky-T1: The Vicuna+Alpaca moment for reasoning
sky-t1-32b-preview qwen-2.5-32b r1 o1-preview gpt-4o claude-3-sonnet bespoke-stratos-32b gemini-2.0-flash-thinking berkeley usc deepseek bespoke-labs google llmsys stanford lm-sys reasoning supervised-finetuning reinforcement-learning multimodality model-distillation context-windows code-execution model-repeatability behavioral-self-awareness rlhf teortaxestex cwolferesearch madiator chakraai philschmid abacaj omarsar0
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.