Locally Coherent Parallel Decoding in Diffusion Language Models
arXiv:2603.20216v1 Announce Type: new Abstract: Diffusion language models (DLMs) have emerged as a promising alternative to autoregressive (AR) models, offering sub-linear generation latency and bidirectional capabilities that are particularly appealing for code generation and editing. Achieving sub-linear latency in discrete DLMs requires predicting multiple tokens in parallel. However, standard DLMs sample tokens independently from conditional marginal distributions, failing to capture the joint dependencies among concurrently generated tokens. As a result, they often lead to syntactic inconsistencies and break multi-token structures. In this work, we introduce CoDiLA (Coherent Diffusion with Local Autoregression), a method that reconciles parallel sampling with local dependency modeling. Rather than forcing the DLM to resolve fine-grained syntax, CoDiLA delegates local decoding to a small, auxiliary AR model operating on the diffusion latents. This design allows for parallel block
arXiv:2603.20216v1 Announce Type: new Abstract: Diffusion language models (DLMs) have emerged as a promising alternative to autoregressive (AR) models, offering sub-linear generation latency and bidirectional capabilities that are particularly appealing for code generation and editing. Achieving sub-linear latency in discrete DLMs requires predicting multiple tokens in parallel. However, standard DLMs sample tokens independently from conditional marginal distributions, failing to capture the joint dependencies among concurrently generated tokens. As a result, they often lead to syntactic inconsistencies and break multi-token structures. In this work, we introduce CoDiLA (Coherent Diffusion with Local Autoregression), a method that reconciles parallel sampling with local dependency modeling. Rather than forcing the DLM to resolve fine-grained syntax, CoDiLA delegates local decoding to a small, auxiliary AR model operating on the diffusion latents. This design allows for parallel block generation while ensuring sequential validity within each block and maintaining core DLM capabilities, including bidirectional modeling across blocks. We demonstrate that using a highly compact auxiliary AR model (e.g., 0.6B parameters) effectively eliminates coherence artifacts, establishing a new Pareto frontier for accuracy and speed in code generation benchmarks.
Executive Summary
This study introduces CoDiLA, a method that reconciles parallel sampling with local dependency modeling in diffusion language models. By delegating local decoding to a small, auxiliary autoregressive model, CoDiLA enables parallel block generation while ensuring sequential validity within each block. The results demonstrate that CoDiLA effectively eliminates coherence artifacts, establishing a new Pareto frontier for accuracy and speed in code generation benchmarks. The compact auxiliary AR model requires only 0.6B parameters, making it a viable alternative to standard DLMs. CoDiLA's design combines the benefits of parallel sampling and local dependency modeling, offering a promising solution for applications such as code generation and editing.
Key Points
- ▸ CoDiLA reconciles parallel sampling with local dependency modeling in diffusion language models.
- ▸ CoDiLA uses a compact auxiliary AR model to enhance local decoding
- ▸ The results demonstrate improved accuracy and speed in code generation benchmarks
Merits
Strength in Addressing Coherence Artifacts
CoDiLA effectively eliminates coherence artifacts, a significant limitation of standard DLMs.
Compact Auxiliary AR Model
The compact auxiliary AR model requires only 0.6B parameters, making it a viable alternative to standard DLMs.
Demerits
Potential Overhead of Auxiliary AR Model
The use of an auxiliary AR model may introduce additional computational overhead, which could impact performance in certain applications.
Limited Exploration of Hyperparameters
The study does not thoroughly explore the effects of varying hyperparameters on the performance of CoDiLA.
Expert Commentary
The introduction of CoDiLA represents a significant advancement in the field of diffusion language models. By addressing the limitations of standard DLMs, CoDiLA offers a promising solution for applications that require both parallel sampling and local dependency modeling. However, further research is needed to fully explore the potential of CoDiLA and to address potential concerns related to the use of auxiliary AR models. Additionally, the study's findings highlight the need for more efficient and effective language models, which could have significant implications for policy and regulation in the AI industry.
Recommendations
- ✓ Future research should explore the effects of varying hyperparameters on the performance of CoDiLA.
- ✓ Developers should consider the potential overhead of auxiliary AR models when implementing CoDiLA in real-world applications.
Sources
Original: arXiv - cs.CL
