Update README.md
Browse files
README.md
CHANGED
|
@@ -30,7 +30,7 @@ datasets:
|
|
| 30 |
|
| 31 |
We propose a <b>S</b>equential <b>D</b>iffusion <b>L</b>anguage <b>M</b>odel (<b>SDLM</b>), to cheaply stimulate the parallel prediction capabilities of diffusion models. Specifically, SDLM reduces distribution shift by limiting the prediction range to a fixed block length and enforces decoding order through the longest prefix decoding method, thereby significantly improving prediction efficiency while ensuring generation quality. Our method can be viewed as a further generalization of the autoregressive (AR) paradigm. Therefore, it is possible to use pre-trained AR weights and quickly migrate to the diffusion framework with only minimal instruction fine-tuning.
|
| 32 |
|
| 33 |
-
, to cheaply stimulate the parallel prediction capabilities of diffusion models. Specifically, SDLM reduces distribution shift by limiting the prediction range to a fixed block length and enforces decoding order through the longest prefix decoding method, thereby significantly improving prediction efficiency while ensuring generation quality. Our method can be viewed as a further generalization of the autoregressive (AR) paradigm. Therefore, it is possible to use pre-trained AR weights and quickly migrate to the diffusion framework with only minimal instruction fine-tuning.
|
| 32 |
|
| 33 |
+
|
| 34 |
|
| 35 |
## SDLM Family
|
| 36 |
|