GLM 5.3 Intuitively Explained: Scaling with Post-training
baseten · x · 2026-08-29
The article introduces GLM-5.3, presenting it as a perfect application of Sutton's Bitter Lesson in Reinforcement Learning. It demonstrates how a model can leapfrog its predecessor without architectural changes, solely by scaling compute through post-training techniques.
Related event: GLM-5.3 Post-Training: Winning by Compute Scaling(2 posts)→
More from Models
- Nathan Lambert slams Meta's Muse Open License as 'trash' — natolambert · 2026-10-16
- Google launches Gemini 4 Argon to Ultra and Pro subscribers, touting physics and 3D skills — ShikharMurty · 2026-10-16
- Google gives X users early access to test Argon, not just companies — lyraxana · 2026-10-16
- Grok Voice 3.0 Tops Design Arena Speech Leaderboard, Up 7.4 Points — XFreeze · 2026-10-16
- Robotics LLMs tested on 16 tasks: GPT-6 Astra hits 17.2% success vs Fable's 9.1% — pabbeel · 2026-10-16
- Meta open-sources Muse Spark: top US open model, scores just below DeepSeek — bindureddy · 2026-10-16