Unverified50% confidenceFactExact time
Unlike autoregressive models such as GPT that generate token by token from left to right, dLLMs do not have a fixed revealing order at inference time and have the potential for parallel decoding and generation in arbitrary order, but this introduces the problem of deciding the decoding order.
1
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50%
Confidence
Long-term
Relevance
10/30/2026
First Seen
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Related Claims
UnverifiedThe OrderGPT paper proposes that a "single adapted order" between left-to-right and arbitrary order is a neglected middle ground, and presents a method that lets the model learn the generation order itself.72% similarUnverifiedOrderGPT's training mechanism includes extracting dependency signals from random prefixes, aggregating them into a dependency graph and deriving priorities, and using a lightweight OrderMLP to predict the order.71% similarUnverifiedGenerative models (such as LLMs like GPT and Claude) generate text word by word through probabilistic sampling, and their outputs are open-ended and diverse, but the same input may yield different results.69% similarUnverifiedThe determinism of LLM inference has preconditions: the inference backend must not introduce extra randomness, such as changes in floating-point accumulation order caused by dynamic batching, non-deterministic GPU operators, or tokenizer version differences.69% similarUnverifiedWhen an LLM call is constrained to choosing an answer from a fixed set of options, it is essentially a classifier rather than a generation task.69% similar
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