LLaDA 2.1

Ant Group releases LLaDA 2.1, a major upgrade to discrete diffusion language models that breaks the speed-quality trade-off through Token-to-Token (T2T) editing. By weaving token editing into the conventional Mask-to-Token decoding scheme, LLaDA 2.1 introduces two configurable modes: Speedy Mode for aggressive throughput and Quality Mode for benchmark-leading accuracy. The release also includes the first large-scale RL framework for diffusion LLMs. - **Editable state evolution:** Unlike standard diffusion models that only unmask tokens, LLaDA 2.1 can also edit already-generated tokens. This dual action space (unmasking + correction) lets the model aggressively draft with low-confidence thresholds and then refine errors in subsequent passes, fundamentally changing the speed-quality trade-off. - **Two operating modes:** Speedy Mode lowers the mask-to-token threshold for maximum throughput, relying on T2T passes to fix errors. Quality Mode uses conservative thresholds for superior benchmark scores. This gives practitioners a configurable knob between speed and accuracy without swapping models. - **Extreme decoding speed:** LLaDA 2.1-Flash (100B) hits 892 tokens per second on HumanEval+ and 801 TPS on BigCodeBench. The Mini variant (16B) reaches a peak of 1,587 TPS. These speeds dramatically outpace autoregressive models of comparable quality. - **First RL for diffusion LLMs:** The paper introduces EBPO (Evidence-Based Policy Optimization), an RL framework that uses block-causal masking and parallel likelihood estimation to enable stable policy optimization at scale for diffusion models. RL training sharpens reasoning and instruction-following across 33 benchmarks.