Add RLHF document

docs/source/generate_examples.py

@@ -14,13 +14,14 @@
 def fix_case(text: str) -> str:
     subs = {
         "api": "API",
-        "Cli": "CLI",
+        "cli": "CLI",
         "cpu": "CPU",
         "llm": "LLM",
         "tpu": "TPU",
         "aqlm": "AQLM",
         "gguf": "GGUF",
         "lora": "LoRA",
+        "rlhf": "RLHF",
         "vllm": "vLLM",
         "openai": "OpenAI",
         "multilora": "MultiLoRA",

docs/source/index.md

@@ -105,6 +105,7 @@ features/compatibility_matrix
 :maxdepth: 1
 
 training/trl.md
+training/rlhf.md
 
 :::
 

docs/source/training/rlhf.md

@@ -0,0 +1,11 @@
+# Reinforcement Learning from Human Feedback
+
+Reinforcement Learning from Human Feedback (RLHF) is a technique that fine-tunes language models using human-generated preference data to align model outputs with desired behaviours.
+
+vLLM can be used to generate the completions for RLHF. The best way to do this is with libraries like [TRL](https://github.com/huggingface/trl), [OpenRLHF](https://github.com/OpenRLHF/OpenRLHF) and [verl](https://github.com/volcengine/verl).
+
+See the following basic examples to get started if you don't want to use an existing library:
+
+- [Training and inference processes are located on separate GPUs (inspired by OpenRLHF)](https://docs.vllm.ai/en/latest/getting_started/examples/rlhf.html)
+- [Training and inference processes are colocated on the same GPUs using Ray](https://docs.vllm.ai/en/latest/getting_started/examples/rlhf_colocate.html)
+- [Utilities for performing RLHF with vLLM](https://docs.vllm.ai/en/latest/getting_started/examples/rlhf_utils.html)