Analyst: Undercooked 'Pro' models stem from weak training signal, not architecture flaws
teortaxesTex · x · 2026-10-25
AI commentator teortaxesTex argues that underwhelming Pro-tier models aren't due to architecture-level flaws in the larger base models, but weak training signals — fewer RL trajectories and worse param/token ratios leave Pros "undercooked," performing closer to Flash-level models.
He believes narrow domains can be fixed with sheer volume (citing Speciale), and that model-generated environments plus new funding will help; he predicts "Pros actually a lot stronger" by Q1 2027.
Related event: DeepSeek Pro Underperforms Due to Weak Training Signals, Not Architecture(2 posts)→
More from Models
- Interconnects podcast: Kimi K3.1's mixed reaction, open-weight cyber risks, expert divides — natolambert · 2026-10-25
- vibesbench: an informal benchmark where models opine on 90s alt-rock angst — firasd · 2026-10-25
- RIP unlimited Claude: Pro user's limits maxed out as Anthropic tightens quotas — prasenx · 2026-10-25
- DeepSeek's design philosophy: compute scales easily, memory doesn't — pstAsiatech · 2026-10-25
- ChatGPT desktop app riddled with errors — generous credits users can't even spend — koltregaskes · 2026-10-25
- Community urges backing up quantized models amid takedown risk, shares torrent mirrors — Kahvana · 2026-10-25