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This paper proposes three training-time interventions (positional weighting, first-error focal loss, and chain loss) to align diffusion-based draft models with autoregressive verification in speculative decoding, improving accepted prefix length by 21–76% without extra inference cost.
This paper studies the gap between synthetic and human data for evaluating LLM personalization across three stages: attribute extraction, relevance matching, and response generation. Results show models perform worse on real human data, and the authors introduce lightweight training interventions to improve alignment.
This paper proposes a framework to evaluate and improve faithfulness of chain-of-thought reasoning by controlling information flow, using entropy-based, KL-divergence, and gradient-based diagnostics, and introduces training interventions (attention masking, gradient masking, adversarial perturbations) that make reasoning more transparent and reduce shortcut reliance.