K2-Think: A Parameter-Efficient Reasoning System
8 months ago
- #Reasoning Systems
- #Machine Learning
- #Parameter Efficiency
- K2-Think is a 32B parameter reasoning system that matches or surpasses larger models like GPT-OSS 120B and DeepSeek v3.1.
- Built on the Qwen2.5 base model, it combines advanced post-training and test-time computation techniques.
- Key technical pillars include Long Chain-of-thought Supervised Finetuning, Reinforcement Learning with Verifiable Rewards (RLVR), and Agentic planning.
- Other techniques include Test-time Scaling, Speculative Decoding, and Inference-optimized Hardware.
- Excels in mathematical reasoning, achieving state-of-the-art scores on public benchmarks for open-source models.
- Strong performance in other areas like Code and Science.
- Freely available with best-in-class inference speeds of over 2,000 tokens per second per request via the Cerebras Wafer-Scale Engine.