Efficient sparse attention plus large, stabilized RL and synthetic agent tasks push an open LLM to near‑frontier reasoning and agent performance, with a high‑compute variant achieving gold‑medal results.
A fast, RL-trained MoE coding agent that brings frontier-level usefulness to real-world development with tools, long context, and production-grade infrastructure.
Tinker is a managed, flexible fine-tuning API for open-weight LLMs—spanning small to massive models—with low-level control, an open-source cookbook, and private beta access starting now.
Evolving plain-English instructions with multi-agent test-time search beats code on ARC and highlights that RL-driven, transferable reasoning is key to AGI.
A safety-focused addendum introduces GPT-5-Codex, an agentic coding model trained on real tasks, widely available, and protected by layered mitigations.
Treat LLM routing as a contextual bandit and use a preference-informed LinUCB plus a knapsack budget policy to adaptively, cost-effectively pick the right model per query.
