docs(golden-al): document SGLang and TRT-LLM synthetic acceptance knobs

.github/codeowner-signoff-verify-prompt.md

@@ -323,13 +323,19 @@ Verify BOTH:
     YAMLs, or exported before launch in benchmark scripts).
   - vLLM: the `--speculative-config` JSON contains BOTH
     `"rejection_sample_method": "synthetic"` and `"synthetic_acceptance_length": <AL>`.
+  - TRT-LLM: env var `TLLM_SPEC_DECODE_FORCE_NUM_ACCEPTED_TOKENS: <AL - 1>` in the
+    server/decode environment. The value counts accepted DRAFT tokens only and
+    EXCLUDES the bonus/verification token, so it must be the golden AL minus 1
+    (fractional values allowed — e.g. golden AL 3.5 -> 2.5).
   FAIL if an agentic spec-decode config runs real (unsimulated) acceptance — name the
   config/script and line.
 - (b) AL VALUE MATCHES THE GOLDEN CURVE. Read the committed golden AL YAML for the
   model in `golden_al_distribution/` (default-branch checkout; e.g. `qwen3.5_mtp.yaml`,
   `kimik2.5_eagle3.yaml`) and confirm the pinned AL equals the golden value for that
   model, thinking mode, and the config's `num_speculative_tokens` / MTP level (e.g.
-  qwen3.5 thinking_on with 3 speculative tokens -> 3.39). A submission may choose any
+  qwen3.5 thinking_on with 3 speculative tokens -> 3.39). For TRT-LLM configs, compare
+  the pinned `TLLM_SPEC_DECODE_FORCE_NUM_ACCEPTED_TOKENS` value PLUS 1 against the
+  golden AL (the env var excludes the bonus token). A submission may choose any
   supported draft length, but it may NOT substitute a different acceptance target.
   FAIL on a mismatch — name the config, the pinned value, and the expected golden
   value. If the model has no committed golden curve yet, do not guess: the sign-off's

golden_al_distribution/README.md

@@ -39,6 +39,20 @@ vllm serve MODEL \
 
 The option was unified across vLLM model runners in [vllm-project/vllm#40662](https://github.com/vllm-project/vllm/pull/40662).
 
+SGLang supports the same policy through its simulated-acceptance environment variables, set in the server environment (the `aggregated_environment` / `decode_environment` sections of srt-slurm YAMLs, or exported before launch in benchmark scripts):
+
+```yaml
+SGLANG_SIMULATE_ACC_LEN: '3.24'   # AL from the committed golden YAML
+SGLANG_SIMULATE_ACC_METHOD: match-expected
+SGLANG_SIMULATE_ACC_TOKEN_MODE: real-draft-token
+```
+
+TensorRT-LLM supports it through [`TLLM_SPEC_DECODE_FORCE_NUM_ACCEPTED_TOKENS`](https://github.com/NVIDIA/TensorRT-LLM/blob/2cbdaa0ffa36fbef7960a0ad9f0458373025fa9f/tensorrt_llm/_torch/speculative/interface.py#L1065-L1078). **Note the off-by-one:** this variable counts accepted *draft* tokens only and excludes the bonus/verification token, so set it to the golden AL **minus 1**. Fractional values are supported — the integer part is accepted every iteration and the fractional part is the probability of accepting one additional draft token. For example, a golden AL of `3.5` becomes:
+
+```bash
+TLLM_SPEC_DECODE_FORCE_NUM_ACCEPTED_TOKENS=2.5
+```
+
 This policy follows the same broad principle as MLPerf Inference: prescribe the workload rules needed for comparable system measurements. InferenceX is evaluating inference-system performance, not the ability to fine-tune a benchmark-specific speculative head.
 
 ## How a golden AL curve is collected

golden_al_distribution/README_zh.md

@@ -39,6 +39,20 @@ vllm serve MODEL \
 
 该选项通过 [vllm-project/vllm#40662](https://github.com/vllm-project/vllm/pull/40662) 在 vLLM 的不同模型运行器中实现统一。
 
+SGLang 通过其模拟接受环境变量支持同一策略。这些变量设置在服务环境中（srt-slurm YAML 的 `aggregated_environment` / `decode_environment` 部分，或在基准脚本中于启动前导出）：
+
+```yaml
+SGLANG_SIMULATE_ACC_LEN: '3.24'   # 来自已提交黄金 YAML 的 AL
+SGLANG_SIMULATE_ACC_METHOD: match-expected
+SGLANG_SIMULATE_ACC_TOKEN_MODE: real-draft-token
+```
+
+TensorRT-LLM 通过 [`TLLM_SPEC_DECODE_FORCE_NUM_ACCEPTED_TOKENS`](https://github.com/NVIDIA/TensorRT-LLM/blob/2cbdaa0ffa36fbef7960a0ad9f0458373025fa9f/tensorrt_llm/_torch/speculative/interface.py#L1065-L1078) 支持该策略。**注意存在差一（off-by-one）：** 该变量只统计被接受的*草稿* token，不包括 bonus/验证 token，因此应设置为黄金 AL **减 1**。支持小数值——整数部分在每次迭代中始终被接受，小数部分是额外接受一个草稿 token 的概率。例如，黄金 AL 为 `3.5` 时应设置：
+
+```bash
+TLLM_SPEC_DECODE_FORCE_NUM_ACCEPTED_TOKENS=2.5
+```
+
 这一策略遵循与 MLPerf Inference 相同的广义原则：规定可比较系统测量所需的工作负载规则。InferenceX 评估的是推理系统性能，而不是微调特定基准推测头的能力。
 
 ## 黄金 AL 曲线如何收集