SequenceMatch
Free while signed in. Answers cite the passages they came from.
Formulates sequence generation as imitation learning, enabling backtracking via a backspace action.
Imitation learning framing: Views autoregressive generation as imitation learning with expert data, opening the door to standard IL techniques.
Backspace action: Introduces a "backspace" action that lets the model undo tokens that led to out-of-distribution sequences.
Compounding error mitigation: Addresses the classical autoregressive problem where small early errors compound catastrophically.
Training innovation: An interesting precursor to later work on self-correcting LLMs and reasoning with error recovery.
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