LLaDA2.1: Speeding Up Text Diffusion via Token Editing — part 2/2

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In Table 3 , we focus on showcasing the speed performance of LLaDA2.1 in S Mode . It can be observed that LLaDA2.1 exhibits significant speed variations across different domains, being highest in the code domain and lowest in instruction following. Specifically, after quantization, LLaDA2.1-flash achieves a peak TPS of 891.74 on HumanEval+, while LLaDA2.1-mini reaches 1586.93 in peak TPS, demonstrating significant speed advantages.

As shown in Table 4 , under the same S Mode setting, Multi-Block Editing (MBE) yields consistent performance improvements across benchmarks for both Flash and Mini variants, at the cost of a modest reduction in throughput. The gains are particularly evident on reasoning and coding tasks, indicating that iterative cross-block refinement effectively corrects local errors and improves global consistency without substantially compromising decoding efficiency.

Figure 3 further illustrates the throughput (in terms of token per sec) comparison of LLaDA 2.1 variants against LLaDA 2.0, Ling, and Qwen-3 across 5 different benchmark domains as shown in Table 3 . This comparison spotlights LLaDA-2.1 (S Mode)’s striking speed advantage: it achieves dramatically faster inference while sacrificing only a negligible sliver of output quality.

## 6 Outlook and Limitation

#### Tradeoff Between Inference Speed and Accuracy

While LLaDA2.1 significantly improves inference speed, a clear speed-accuracy tradeoff persists, particularly with noticeable performance differences across various domains. It is necessary to adjust threshold parameters for different domains to balance speed and accuracy. In structured-data fields such as code and math, setting S Mode achieves high speed with little accuracy loss. However, in some general chat cases, these settings can cause undesirable output. In such cases, we recommend adjusting the parameters to Q Mode . Our conjecture is that this pattern may be related to the model’s inherent preference for structured data or the distributional characteristics of training dataset. Further validation will be conducted in our future research.

#### Editable Enhanced dLLM

Although dLLMs inherently support high parallelism, theoretically offering speed advantages over AR models, our experimental observations show that this high parallelism also introduces a higher error rate compared to AR models. These hidden errors can reduce the model’s confidence in subsequent reasoning, ultimately slowing down the overall process. Therefore, timely editing to correct errors is essential. In our case analysis of LLaDA2.1, we observed that prompt editing corrected decoding errors, helping to maintain higher inference speeds. However, research on the editing capabilities of dLLMs is still in its early stages. We anticipate that future work, such as integrating editing into reinforcement learning, will further enhance the performance of editable dLLMs.

#### LLaDA2.1 remains in an experimental phase. Although rare, certain edge cases may occur.

Empirical observations show that aggressively lowering the masking threshold τ mask \tau_{\text{mask}} can quickly generate “rough drafts”. Although the model’s self-correction can partially alleviate the “stuttering” artifacts (such as n-gram repetitions) caused by independent parallel sampling, balancing drafting speed with the quality of the initial structure remains a key operational frontier. Overall, by unifying dynamic inference, hybrid training, and principled reinforcement learning, our work establishes a solid foundation for self-correcting discrete diffusion language models.

#### Conclusion

Overall, LLaDA2.1 introduces an editing feature, which, through cumulative error correction, significantly lowered the decoding threshold of the dLLM and yielded considerable inference speed benefits. However, this model still faces many unresolved issues, and we anticipate that more powerful editable dLLMs will deliver even more unexpected and impressive results.