K2-Think
A 32B-parameter system built on Qwen2.5 that rivals or beats far larger models on hard math by combining long CoT SFT, RL with verifiable rewards, lightweight test-time scaffolding, and inference optimization.
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Six-pillar recipe that stacks, not bloats. Long chain-of-thought SFT → RL with verifiable rewards (Guru across Math/Code/Science/Logic/Simulation/Tabular) → “Plan-Before-You-Think” prompt restructuring → Best-of-N=3 selection → speculative decoding → deployment on Cerebras WSE.
Frontier math at small scale. On AIME-24/25, HMMT-25, and Omni-MATH-HARD, K2-Think achieves a math micro-average of 67.99, exceeding open baselines like DeepSeek v3.1 and GPT-OSS 120B, while using a fraction of the parameters.
Test-time scaffolding gives most of the lift. From the SFT+RL checkpoint, Best-of-3 delivers the biggest single gain, and combining it with planning yields another bump. The same planning also shortens answers by up to ~12 percent on hard tasks.
Practical speed for long reasoning. Cerebras WSE plus speculative decoding pushes ≈2,000 tokens/s per request, turning 32k-token chains into seconds-level interactions rather than minutes. This keeps multi-sample pipelines interactive.
Training insights and safety profile. RL from a strong SFT checkpoint improves less than RL from base, and shortening max response length mid-training hurts performance. Safety evaluation yields a Safety-4 macro score of 0.75, with strong refusal and conversational robustness but work to do on cybersecurity and jailbreak resistance.