Uploaded November 2025 | Updated September 2026, 2 weeks ago
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![[State of Post-Training] From GPT-4.1 to 5.1: RLVR, Agent & Token Efficiency — Josh McGrath, OpenAI](https://i.ytimg.com/vi/botHQ7u6-Jk/mqdefault.jpg "[State of Post-Training] From GPT-4.1 to 5.1: RLVR, Agent & Token Efficiency — Josh McGrath, OpenAI
From pre-training data curation to shipping *GPT-4o,* *o1,* *o3,* and now *GPT-5 thinking* and the *shopping model,* *Josh McGrath* has lived through the full arc of OpenAIs post-training evolution—from the PPO vs DPO debates of 2023 to todays RLVR era, where the real innovation isnt optimization methods but *data quality, signal trust, and token efficiency.* We sat down with Josh at *NeurIPS 2025* to dig into the state of post-training heading into 2026: why RLHF and RLVR are both just policy gradient methods (the difference is the input data, not the math), how *GRPO* from DeepSeek Math was underappreciated as a shift toward more trustworthy reward signals (math answers you can verify vs. human preference you cant), why *token efficiency* matters more than wall-clock time (GPT-5 to 5.1 bumped evals _and_ slashed tokens), how *Codex* has changed his workflow so much he feels trapped by 40-minute design sessions followed by 15-minute agent sprints, the infrastructure chaos of scaling RL (way more moving parts than pre-training), why *long context* will keep climbing but agents + graph walks might matter more than 10M-token windows, the *shopping model* as a test bed for interruptability and chain-of-thought transparency, why *personality toggles* (Anton vs Clippy) are a real differentiator users care about, and his thesis that the education system isnt producing enough people who can do *both distributed systems and ML research*—the exact skill set required to push the frontier when the bottleneck moves every few weeks.
We discuss:
* Joshs path: *pre-training data curation → post-training researcher at OpenAI,* shipping GPT-4o, o1, o3, GPT-5 thinking, and the shopping model
* Why he switched from pre-training to post-training: Do I want to make 3% compute efficiency wins, or change behavior by 40%?
* The *RL infrastructure challenge:* way more moving parts than pre-training (tasks, grading setups, external partners), and why babysitting runs at 12:30am means jumping into unfamiliar code constantly
* How *Codex* has changed his workflow: 40-minute design sessions compressed into 15-minute agent sprints, and the strange trapped feeling of waiting for the agent to finish
* The *RLHF vs RLVR debate:* both are policy gradient methods, the real difference is *data quality and signal trust* (human preference vs. verifiable correctness)
* Why *GRPO* (from DeepSeek Math) was underappreciated: not just an optimization trick, but a shift toward reward signals you can actually trust (math answers over human vibes)
* The *token efficiency revolution:* GPT-5 to 5.1 bumped evals _and_ slashed tokens, and why thinking in tokens (not wall-clock time) unlocks better tool-calling and agent workflows
* *Personality toggles:* Anton (tool, no warmth) vs Clippy (friendly, helpful), and why Josh uses custom instructions to make his model just a tool
* The *router problem:* having a router at the top (GPT-5 thinking vs non-thinking) _and_ an implicit router (thinking effort slider) creates weird bumps, and why the abstractions will eventually merge
* *Long context:* climbing Graph Blocks evals, the dream of 10M+ token windows, and why agents + graph walks might matter more than raw context length
* Why the education system isnt producing enough people who can do *both distributed systems and ML research,* and why thats the bottleneck for frontier labs
* The 2026 vision: *neither pre-training nor post-training is dead,* were in the fog of war, and the bottleneck will keep moving (so emotional stability helps)
—
Josh McGrath
* OpenAI: https://openai.com
* https://x.com/j_mcgraph
00:00:00 Introduction: Josh McGrath on Post-Training at OpenAI
00:04:37 The Shopping Model: Black Friday Launch and Interruptability
00:07:11 Model Personality and the Anton vs Clippy Divide
00:08:26 Beyond PPO vs DPO: The Data Quality Spectrum in RL
00:01:40 Infrastructure Challenges: Why Post-Training RL is Harder Than Pre-Training
00:13:12 Token Efficiency: The 2D Plot That Matters Most
00:03:45 Codex Max and the Flow Problem: 40 Minutes of Planning, 15 Minutes of Waiting
00:17:29 Long Context and Graph Blocks: Climbing Toward Perfect Context
00:21:23 The ML-Systems Hybrid: Whats Hard to Hire For
00:24:50 Pre-Training Isnt Dead: Living Through Technological Revolution")
