Josh McGrath

[State of Post-Training] From GPT-4.1 to 5.1: RLVR, Agent & Token Efficiency — Josh McGrath, OpenAI

Dec 31, 2025 · 27:34

Josh McGrath, an OpenAI post-training researcher, states that real post-training innovation lies in data quality and signal trust, not optimization methods, with RLVR and token efficiency central. He moved from pre-training (3% compute gains) to post-training (40% behavior change), describes the infrastructure chaos of RL runs, and cites GRPO from DeepSeek Math as underappreciated for providing verifiable reward signals. GPT-5 to GPT-5.1 bumped evals while slashing tokens, emphasizing token efficiency over wall-clock time. The shopping model features interruptibility and chain-of-thought transparency, and personality toggles (Anton vs Clippy) are a key differentiator. For long context, he argues agents with graph walks may be more important than 10M-token windows. He concludes that the education system fails to produce people skilled in both distributed systems and ML research, a critical combination as bottlenecks shift.

GPT 4.1: The New OpenAI Workhorse

Apr 15, 2025 · 45:04

OpenAI's Michelle Pokrass and Josh McGrath join hosts Alessio, Swyx, and Sean to launch GPT 4.1, a new model family positioned as the go-to workhorse for developers with major improvements in coding, instruction following, and long context. The three models—4.1, 4.1 Mini, and 4.1 Nano—introduce a 1M-token context window, 75% prompt caching discount, and cheaper pricing than 4o. Coding gains are highlighted by 55% on SWE-bench (vs o1's 41%), while instruction following benefits from real-world API data and new evals like GraphWalk for multi-hop reasoning. The team explains that most improvements come from post-training techniques, with vision capabilities lifted by a new pre-trained base. Fine-tuning is available day one, and developers are encouraged to opt in to data sharing for future model iterations.