Instructions to use code-critic-model/Qwen3-8B-Critic-SFT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use code-critic-model/Qwen3-8B-Critic-SFT with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="code-critic-model/Qwen3-8B-Critic-SFT")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("code-critic-model/Qwen3-8B-Critic-SFT")
model = AutoModelForCausalLM.from_pretrained("code-critic-model/Qwen3-8B-Critic-SFT", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use code-critic-model/Qwen3-8B-Critic-SFT with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "code-critic-model/Qwen3-8B-Critic-SFT"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "code-critic-model/Qwen3-8B-Critic-SFT",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/code-critic-model/Qwen3-8B-Critic-SFT

SGLang

How to use code-critic-model/Qwen3-8B-Critic-SFT with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "code-critic-model/Qwen3-8B-Critic-SFT" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "code-critic-model/Qwen3-8B-Critic-SFT",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "code-critic-model/Qwen3-8B-Critic-SFT" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "code-critic-model/Qwen3-8B-Critic-SFT",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use code-critic-model/Qwen3-8B-Critic-SFT with Docker Model Runner:
```
docker model run hf.co/code-critic-model/Qwen3-8B-Critic-SFT
```

Qwen3-8B-Critic-SFT

The main 8B critic from Steer, Don't Solve: Training Small Critic Models for Large Code Agents.

A critic sits next to a frozen coding agent. Every k agent steps it reads the trajectory so far and returns a short structured critique: which error categories it detects, the evidence, a recovery action, the task status, and one line of overall guidance. It steers the agent; it does not write the patch. This model is Qwen3-8B fine-tuned on critiques written by Claude Opus 4.6 for trajectories of two different agents.

All released models and datasets are listed on the organization page. Code, configs, and launch scripts are in the critic-training repository.

Where it appears in the paper

Paper location	Row label
Table 1, every agent block	`Qwen3-8B + SFT`
Table 2, Multi-SWE-bench	`+ SFT`
Table 3, corpus ablation	`8B, Qwen+CWM`
Table 4, prompt ablation	`+ SFT (High-Level, Ours)`
Figure 4, cost versus resolve rate	the 8B SFT critic marker for each agent

Original run name: qwen3-8b-full-sft-prm-r2egym-swebench-instructions-k5-cwm-plus-qwen-opus-distill-32k-lr5e6-multiturn. This is the name that appears in the repository's configs, logs, result directories, and the LiteLLM cost registry (there under the shubhamrgandhi/ prefix).

Training data

code-critic-model/critic-sft-cwm-qwen, 6,447 examples.

Tasks: 500 R2E-Gym instances from the matplotlib, moto, and sympy repositories. These are disjoint from SWE-bench Verified at the instance level.
Agents that produced the trajectories: CWM-32B (500 trajectories, 4,532 examples) and Qwen3-Next-80B-A3B-Instruct (483 trajectories, 1,915 examples).
Teacher: Claude Opus 4.6, queried every 5 agent steps with the high-level prompt. The high-level prompt asks for error detection and short guidance and forbids full code solutions. The paper calls this the "high-level" or "concise" prompt.

Training setup

Full-parameter SFT with LLaMA-Factory. The config is finetuning/qwen3_8b_critic_full_sft_l40s_train_multiturn_resumable.yaml in the repository.

Setting	Value
Base model	Qwen/Qwen3-8B
Chat template	`qwen3_nothink` (thinking disabled at training and inference)
Sequence length	32,768 tokens
Loss	final critique turn only (`mask_history: true`)
Hardware	8 x L40S, per-device batch 1, effective batch 8
Optimizer	AdamW, lr 5e-6, cosine schedule, warmup ratio 0.1
Epochs	3
Precision	bf16

Results

Resolve rate on SWE-bench Verified (500 instances), no critic versus this critic. Numbers are from Table 1 of the paper and take the better of k=5 and k=10 for each configuration.

Coding agent	No critic	+ Qwen3-8B-Critic-SFT
Qwen3-32B	8.8	13.8
Qwen3-Next-80B-A3B	20.0	25.2
GPT-OSS-20B	3.0	13.0
GLM-4.7-Flash-30B-A3B	21.6	37.6
GPT-OSS-120B (medium reasoning)	20.4	31.4
o3-mini	19.0	29.4

Resolve rate on 300 Multi-SWE-bench instances (Table 2), with k=5. The critic saw only Python trajectories during training.

Coding agent	No critic	+ Qwen3-8B-Critic-SFT
Qwen3-Next-80B-A3B	9.7	11.7
Qwen3-32B	1.3	3.3

How to use

The critic was served with vLLM in bf16 and called through the repository's fork of mini-swe-agent, which inserts a critique into the agent's context every k steps.

vllm serve code-critic-model/Qwen3-8B-Critic-SFT \
    --served-model-name Qwen3-8B-Critic-SFT \
    --dtype bfloat16 --max-model-len 65536 --port 8071

Then, from the repository root, run an agent with the step-aware critic prompt at k=5:

bash scripts/run_critic_max150.sh prm_issue_res_instructions_step_aware 5 0 qwen3-80b \
    --prm Qwen3-8B-Critic-SFT --prm-node <vllm-host>:8071 --slice :500 \
    --prefix-dir <path to the matching no-critic run>

The launcher passes the --prm name to LiteLLM, which needs a matching entry in mini-swe-agent/configs/litellm_model_registry.json to price the calls. Copy the block for the original run name to a new key Qwen3-8B-Critic-SFT, or serve under the original run name instead. Without a registry entry the critic call fails and the agent runs without critiques. The full inference procedure, including the agent-side configs and the no-AWS path, is in QUICKSTART.md and HANDOVER.md.

To call the critic directly, reuse a training record as the prompt. The system message and the trajectory encoding are exactly what the model saw during training.

from datasets import load_dataset
from transformers import AutoTokenizer, AutoModelForCausalLM

repo = "code-critic-model/Qwen3-8B-Critic-SFT"
tok = AutoTokenizer.from_pretrained(repo)
model = AutoModelForCausalLM.from_pretrained(repo, torch_dtype="bfloat16", device_map="auto")

record = load_dataset("code-critic-model/critic-sft-cwm-qwen", split="train")[0]
messages = record["messages"][:-1]   # drop the teacher critique, keep system + trajectory
inputs = tok.apply_chat_template(messages, add_generation_prompt=True, enable_thinking=False,
                                 return_tensors="pt").to(model.device)
out = model.generate(inputs, max_new_tokens=1024, do_sample=False)
print(tok.decode(out[0][inputs.shape[1]:], skip_special_tokens=True))

Limitations

The critic was trained on Python repositories only and on trajectories in the mini-swe-agent format (one bash command per step). It has been evaluated as a critic for other agents, not as a stand-alone coder, and its critiques are only as reliable as the teacher's on the training distribution.

Citation

@misc{gandhi2026steerdontsolvetraining,
  title={Steer, Don't Solve: Training Small Critic Models for Large Code Agents},
  author={Shubham Gandhi and Yiqing Xie and Atharva Naik and Ruichen Zhu and Carolyn Rose},
  year={2026},
  eprint={2606.21811},
  archivePrefix={arXiv},
  primaryClass={cs.SE},
  url={https://arxiv.org/abs/2606.21811}
}