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trl/trainer/grpo_config.py

@@ -80,9 +80,8 @@ class GRPOConfig(TrainingArguments):
             `vllm_gpu_memory_utilization`, leading to a reduced KV cache size. If not set, vLLM will use the model
             context size, which might be much larger than the KV cache, leading to inefficiencies.
         vllm_enable_prefix_caching (`bool`, *optional*, defaults to `True`):
-            Whether to enable prefix caching in vLLM. If set to `True` (default), ensure that the GPU used support
-            this feature, because enabling prefix cache on GPUs older than Ampere architecture (like the V100) may
-            cause errors, see: https://github.com/huggingface/trl/issues/2798.
+            Whether to enable prefix caching in vLLM. If set to `True` (default), ensure that the model and the hardware
+            support this feature.
         vllm_guided_decoding_regex (`str` or `None`, *optional*, defaults to `None`):
             Regex for vLLM guided decoding. If `None` (default), guided decoding is disabled.
 
@@ -211,9 +210,8 @@ class GRPOConfig(TrainingArguments):
     vllm_enable_prefix_caching: Optional[bool] = field(
         default=True,
         metadata={
-            "help": "Whether to enable prefix caching in vLLM. If set to `True` (default), ensure that the GPU used "
-            "support this feature, because enabling prefix cache on GPUs older than Ampere architecture (like the V100) "
-            "may cause errors, see: https://github.com/huggingface/trl/issues/2798."
+            "help": "Whether to enable prefix caching in vLLM. If set to `True` (default), ensure that the model and "
+            "the hardware support this feature."
         },
     )
     vllm_guided_decoding_regex: Optional[str] = field(