aiqbench analysis
This article primarily examines the evolution of post-training methodologies for frontier large language models, specifically analyzing the shift from standard SFT pipelines to advanced techniques like reasoning-based reinforcement learning. It details how recent open-source recipes have formalized distinct stages such as DPO and verifiable rewards, contrasting them with closed proprietary approaches that utilize extensive RLHF iterations. The discussion centers on technical trajectories in model optimization rather than consumer applications or general software updates.
Frontier post-training recipe review with Finbarr Timbers

"Interview" #18
As I’ve been recapping fundamentals of post-training to wrap up my RLHF / Post-training book I knew I needed to get Finbarr Timbers back on the podcast to talk about the state of play. Over the last few months we’ve had many discussions on what we’d need to do to take an Olmo-style recipe to the frontier, supported by Finbarr’s extensive reading of recent model technical reports. To prepare for this, I put together a summary slide deck on the key post-training recipes historically — the path from InstructGPT to today — and today — the key open frontier models. This deck is summarized below as the technical summary, but we do spend 20-35 minutes on it in the podcast, so watching on YouTube is likely the best experience for this one. I previously interviewed Finbarr in December of 2024, shortly after the release of o1 and Tülu 3 (and before he joined Ai2) on the “We are so back” era of RL. 23:00 2026’s model recipes (MiMo Flash, DeepSeek V4, GLM 5, Kimi K2.6, etc.) 23:00 2026’s model recipes (MiMo Flash, DeepSeek V4, GLM 5, Kimi K2.6, etc.) Listen on Apple Podcasts, Spotify, and where ever you get your…
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