2 days ago
- Introduction of reasoning models with multiple effort modes similar to GPT-5.6.
- Definition of reasoning models as generating intermediate reasoning traces.
- Training methods: RLVR (Reinforcement Learning with Verifiable Rewards) and supervised fine-tuning.
- Inference scaling: Adjusting compute and token usage to improve performance.
- Implementation of reasoning effort levels using system prompts and length penalties.
- Overview of effort modes in open-weight models like DeepSeek V4, Nemotron, Kimi, GLM-5, Qwen3, and Inkling.
- Automatic effort selection as a future goal, though currently user-controlled via system prompts.