Self-distillation
Self-distillation improves model performance in early training steps, then gradually declines as the model overfits to its own imperfect signal.
Memories
Fragments, fleeting thoughts, and small moments worth keeping anyway.
Self-distillation improves model performance in early training steps, then gradually declines as the model overfits to its own imperfect signal.
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.