[DLLM] Add JointThreshold algorithm for joint M2T and T2T decoding (#18171)

Signed-off-by: Junlin Zhou <zhoujunlin.zjl@antgroup.com>
Co-authored-by: Tiwei Bie <tiwei.btw@antgroup.com>

docs/supported_models/text_generation/diffusion_language_models.md

@@ -6,6 +6,8 @@ Diffusion language models have shown promise for non-autoregressive text generat
 
 ## Example Launch Command
 
+SGLang supports different DLLM algorithms such as `LowConfidence` and `JointThreshold`.
+
 ```shell
 python3 -m sglang.launch_server \
   --model-path inclusionAI/LLaDA2.0-mini \ # example HF/local path
@@ -17,6 +19,10 @@ python3 -m sglang.launch_server \
 
 ## Example Configuration File
 
+Depending on the algorithm selected, the configuration parameters vary.
+
+LowConfidence Config:
+
 ```yaml
 # Confidence threshold for accepting predicted tokens
 # - Higher values: More conservative, better quality but slower
@@ -28,6 +34,25 @@ threshold: 0.95
 block_size: 32
 ```
 
+JointThreshold Config:
+
+```yaml
+# Decoding threshold for Mask-to-Token (M2T) phase
+# - Higher values: More conservative, better quality but slower
+# - Lower values: More aggressive, faster but potentially lower quality
+# Range: 0.0 - 1.0
+threshold: 0.5
+# Decoding threshold for Token-to-Token (T2T) phase
+# Range: 0.0 - 1.0
+# Setting to 0.0 allows full editing (recommended for most cases).
+edit_threshold: 0.0
+# Max extra T2T steps after all masks are removed. Prevents infinite loops.
+max_post_edit_steps: 16
+# 2-gram repetition penalty (default 0).
+# An empirical value of 3 is often sufficient to mitigate most repetitions.
+penalty_lambda: 0
+```
+
 ## Example Client Code Snippet
 
 Just like other supported models, diffusion language models can be used via the REST API or Python client.