Podbit · Dwarkesh Podcast
Dario Amodei noted that training at short context lengths and serving at long ones can cause performance degradation. Dwarkesh reads this as evidence that RLVR generalization is not infinite — a crack in the foundation of the labs' AGI bet.
Where this was said
At 6:55 · chapter starts 6:10
Dwarkesh questions whether RLVR generalizes from containerized tasks to complex real-world domains like politics or business, citing Dario Amodei's context-length degradation comment as a key data point [1] — Dwarkesh Patel "Dario Amodei noted that training at short context lengths and serving at long ones can cause performance degradation. Dwarkesh reads this a…" 06:55 .
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