/Tech21h ago

ICML 2026 Paper Studies Weak-To-Strong Generalization For Stronger AI Models

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Arda Uzunoğlu
Daniel Khashabi 🕊️#1118
Original postDaniel Khashabi 🕊️#1118
Arda Uzunoğlu@aardauzunoglu

As LLMs surpass humans on many fronts, how can we keep training stronger models?

Our ICML 2026 paper studies this via weak-to-strong generalization and shows that learning when to trust the weak teacher may be key.

Trust Functions: Near-Lossless Weak-to-Strong Generalization 🧵

Anthropic@AnthropicAI

Our internal data shows Claude is accelerating AI development—a possible path to recursive self-improvement, or AI autonomously building a more capable successor.

It’s happening faster than we thought, and the implications deserve greater attention. https://www.anthropic.com/institute/recursive-self-improvement

3:56 PM · Jun 9, 2026 · 1.8K Views
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