Reward models

These models output a scalar reward score or classification result, often used in reinforcement learning or content moderation tasks. They are executed with --is-embedding and some may require --trust-remote-code.

Example launch Command

python3 -m sglang.launch_server \
  --model-path Qwen/Qwen2.5-Math-RM-72B \  # example HF/local path
  --is-embedding \
  --host 0.0.0.0 \
  --tp-size=4 \                          # set for tensor parallelism
  --port 30000 \

Supported models

Model Family (Reward)	Example HuggingFace Identifier	Description
Llama (3.1 Reward / `LlamaForSequenceClassification`)	`Skywork/Skywork-Reward-Llama-3.1-8B-v0.2`	Reward model (preference classifier) based on Llama 3.1 (8B) for scoring and ranking responses for RLHF.
Gemma 2 (27B Reward / `Gemma2ForSequenceClassification`)	`Skywork/Skywork-Reward-Gemma-2-27B-v0.2`	Derived from Gemma‑2 (27B), this model provides human preference scoring for RLHF and multilingual tasks.
InternLM 2 (Reward / `InternLM2ForRewardMode`)	`internlm/internlm2-7b-reward`	InternLM 2 (7B)–based reward model used in alignment pipelines to guide outputs toward preferred behavior.
Qwen2.5 (Reward - Math / `Qwen2ForRewardModel`)	`Qwen/Qwen2.5-Math-RM-72B`	A 72B math-specialized RLHF reward model from the Qwen2.5 series, tuned for evaluating and refining responses.
Qwen2.5 (Reward - Sequence / `Qwen2ForSequenceClassification`)	`jason9693/Qwen2.5-1.5B-apeach`	A smaller Qwen2.5 variant used for sequence classification, offering an alternative RLHF scoring mechanism.

Classification Models

How to Support New Models

⌘I

Basic Usage

Advanced Features

Supported Models

Developer Guide

References

Example launch Command

Supported models

​Example launch Command

​Supported models

Example launch Command

Supported models