OpenHands Critic 4B v1.0

A 4B parameter critic model for evaluating AI agent trajectories, trained to predict behavioral rubrics and task success.

Docs (Use it in OpenHands Software Agent SDK): https://docs.openhands.dev/sdk/guides/critic
Docs (Use it in OpenHands CLI): https://docs.openhands.dev/openhands/usage/cli/critic

Model Details

Base Model: Qwen/Qwen3-4B
Training: Full-parameter fine-tuning with BCE loss
Context Length: Trained on 64K, supports up to 256K tokens
Task: Multi-label classification (26 labels: 25 rubric features + 1 success prediction)

Serving (vLLM Classification API)

We serve this model using vLLM’s classification task:

vllm serve <MODEL_PATH> \
  --host 0.0.0.0 \
  --port 8000 \
  --api-key <API_KEY> \
  --served-model-name <MODEL_NAME> \
  --task classify \
  --max-model-len 262144 \
  --dtype bfloat16 \
  --trust-remote-code \
  --enable-prefix-caching

Usage

We recommend using the OpenHands SDK for inference instead of calling the vLLM classification endpoint directly.

Follow the SDK guide: https://docs.openhands.dev/sdk/guides/critic

In particular, reuse the SDK client implementation here (it already handles formatting and API calls): https://github.com/OpenHands/software-agent-sdk/blob/main/openhands-sdk/openhands/sdk/critic/impl/api/critic.py

At a high level, you will:

Start a critic server (see Serving section above)
Configure the SDK to point to your critic endpoint + API key
Call the SDK critic to score trajectories (returns rubric probabilities + success score)

Citation

@misc{wang2026rubricsupervisedcriticsparserealworld,
      title={A Rubric-Supervised Critic from Sparse Real-World Outcomes}, 
      author={Xingyao Wang and Valerie Chen and Heng Ji and Graham Neubig},
      year={2026},
      eprint={2603.03800},
      archivePrefix={arXiv},
      primaryClass={cs.AI},
      url={https://arxiv.org/abs/2603.03800}, 
}