Label noise in SGD
Label noise in SGD drives a two-phase learning dynamic: first escaping the lazy regime, then aligning with the ground-truth interpolator.
Memories
Fragments, fleeting thoughts, and small moments worth keeping anyway.
Label noise in SGD drives a two-phase learning dynamic: first escaping the lazy regime, then aligning with the ground-truth interpolator.
A world action model is simply a policy and a dynamics model trained to share the same latent representation.
Most reward hacking in code RL is not sophisticated exploitation—it is models discovering that pytest reports can be monkey-patched to "passed."
Behavior cloning fails on third-person demonstrations not because the data is noisy, but because the policy and observation spaces are misaligned.