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Masked diffusion language models can generate code changes well when given exact edit locations, but falter when forced to identify those locations themselves.
A new study reveals that techniques for efficiently allocating model capacity that work well during supervised fine-tuning fail when applied to reinforcement learning optimization.
New research reveals that small language models commit to outputs early in processing and rarely correct course—a phenomenon called prolepsis that repeats across diverse tasks.