Instructions to use PKU-Wu-Lab/LTE-Qwen3-8B-Base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use PKU-Wu-Lab/LTE-Qwen3-8B-Base with Transformers:

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

pipe = pipeline("text-generation", model="PKU-Wu-Lab/LTE-Qwen3-8B-Base")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("PKU-Wu-Lab/LTE-Qwen3-8B-Base")
model = AutoModelForCausalLM.from_pretrained("PKU-Wu-Lab/LTE-Qwen3-8B-Base")
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 PKU-Wu-Lab/LTE-Qwen3-8B-Base with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/PKU-Wu-Lab/LTE-Qwen3-8B-Base

SGLang

How to use PKU-Wu-Lab/LTE-Qwen3-8B-Base 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 "PKU-Wu-Lab/LTE-Qwen3-8B-Base" \
    --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": "PKU-Wu-Lab/LTE-Qwen3-8B-Base",
		"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 "PKU-Wu-Lab/LTE-Qwen3-8B-Base" \
        --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": "PKU-Wu-Lab/LTE-Qwen3-8B-Base",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use PKU-Wu-Lab/LTE-Qwen3-8B-Base with Docker Model Runner:
```
docker model run hf.co/PKU-Wu-Lab/LTE-Qwen3-8B-Base
```

LTE-Qwen3-8B-Base

Introduction

LTE (Learning to reason from Trial and Error) is an RLVR (Reinforcement Learning with Verifiable Rewards) approach presented in the paper Do Not Step Into the Same River Twice: Learning to Reason from Trial and Error.

LTE mitigates the exploration stagnation of Language Models (LMs) by utilizing their previously self-made mistakes as hints, requiring no external expert guidance. It improves the performance upper bound of LMs and enhances both exploitation and exploration during training.

Key Highlights

Self-generated Hints: LTE uses the errors generated by the LMs themselves during training as hints.
No External Expert Guidance: LTE does not require any external expert guidance to mitigate the exploration stagnation of LMs.

Inference

Here is an example of using LTE models for inference:

from transformers import AutoTokenizer
from vllm import LLM, SamplingParams

model_path="JamyDohrn/LTE-Qwen3-8B-Base"

question = "which number is larger? 9.11 or 9.9?"

tokenizer = AutoTokenizer.from_pretrained(model_path)
messages = [{"role": "user", "content": question}]
chat = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)

llm = LLM(model=model_path)
params = SamplingParams(temperature=0.6, max_tokens=32768)
outputs = llm.generate([chat], params)
print(outputs[0].outputs[0].text)

Acknowledgements

LTE is built on the following repositories and we thank their teams for their valuable contributions to the community:

Citation

If you find our work useful, feel free to cite our paper:

@misc{tang2026steprivertwicelearning,
      title={Do Not Step Into the Same River Twice: Learning to Reason from Trial and Error}, 
      author={Chenming Tang and Hsiu-Yuan Huang and Weijie Liu and Clive Bai and Saiyong Yang and Yunfang Wu},
      year={2026},
      eprint={2510.26109},
      archivePrefix={arXiv},
      primaryClass={cs.LG},
      url={https://arxiv.org/abs/2510.26109}, 
}