Instructions to use Kwai-Klear/GoLongRL-30B-A3B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Kwai-Klear/GoLongRL-30B-A3B with Transformers:

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

pipe = pipeline("text-generation", model="Kwai-Klear/GoLongRL-30B-A3B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Kwai-Klear/GoLongRL-30B-A3B")
model = AutoModelForCausalLM.from_pretrained("Kwai-Klear/GoLongRL-30B-A3B")
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

vLLM

How to use Kwai-Klear/GoLongRL-30B-A3B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Kwai-Klear/GoLongRL-30B-A3B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Kwai-Klear/GoLongRL-30B-A3B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Kwai-Klear/GoLongRL-30B-A3B

SGLang

How to use Kwai-Klear/GoLongRL-30B-A3B 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 "Kwai-Klear/GoLongRL-30B-A3B" \
    --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": "Kwai-Klear/GoLongRL-30B-A3B",
		"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 "Kwai-Klear/GoLongRL-30B-A3B" \
        --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": "Kwai-Klear/GoLongRL-30B-A3B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Kwai-Klear/GoLongRL-30B-A3B with Docker Model Runner:
```
docker model run hf.co/Kwai-Klear/GoLongRL-30B-A3B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

✨ GoLongRL-30B-A3B

We present GoLongRL, a fully open-source, capability-oriented post-training recipe for long-context reinforcement learning with verifiable rewards (RLVR).

Resource	Link
📝 Paper	ArXiv 2605.19577
🤗 Daily Paper	Hugging Face Paper
💻 Code	GitHub Repository
📂 Project	HF Collection
🤗 Model Hub	GoLongRL-4B
🤗 Dataset Hub	GolongRL Dataset
📧 Contact	xiao_xuan_zi_666@163.com & suzhenpeng13@163.com

📌 Overview

_{Overall performance comparison on long-context benchmarks (DocMath, LongBench-V2, Frames, MRCR, CorpusQA, LBV1-QA).}

GoLongRL-30B-A3B achieves strong long-context performance at the 30B scale.

Model	Avg.	DocMath	LBV2	Frames	MRCR	CorpusQA	LBV1-QA
Qwen3-30B-A3B-Thinking-2507	60.1	63.3	48.7	70.2	41.6	70.5	66.5
DeepSeek-R1-0528	68.7	63.4	59.5	76.9	64.9	77.5	69.9
Qwen3-235B-A22B-Thinking	68.5	65.8	57.5	75.1	66.2	75.3	70.9
Gemini-2.5-Flash-Thinking	68.7	64.8	56.8	65.8	78.8	79.4	66.9
QwenLong-L1.5 (w. GRPO)	67.2	65.1	55.3	71.4	66.9	76.9	67.9
GoLongRL-30B-A3B (Ours)	69.8	65.3	55.1	74.5	81.6	73.6	68.7

Our framework combines the following:

Capability-Oriented Dataset (23K samples, 9 task types). Guided by a taxonomy of long-context capabilities, the dataset covers precise retrieval, comprehension, exhaustive retrieval, numerical reasoning, structured extraction, structured matching, graded ranking, sequence ordering, and summarization. Each task is paired with its natural evaluation metric as the reward function.
TMN-Reweight. To address optimization challenges from heterogeneous rewards, we propose TMN-Reweight, which combines task-level mean normalization for cross-task reward scale alignment with difficulty-adaptive weighting for more reliable advantage estimation.
Full Open Release. We publicly release the complete dataset, the four-phase construction pipeline, and all training code.

Key Results

Under the same vanilla GRPO setup, our dataset alone outperforms the closed-source QwenLong-L1.5 dataset at both 4B and 30B scales.
TMN-Reweight further improves average performance over vanilla GRPO, with general capabilities preserved or improved across reported evaluations.
Substantial gains on dialogue memory (LongMemEval +13.6) and agentic memory benchmarks.

🔍 Evaluation

Evaluation uses QwenLong-Benchmarks, covering three capability dimensions:

Dimension	Benchmarks
Long-Context	LongBench-V2, MRCR (≤128K / 128K–512K / 512K–1M), Frames, LongBench QA, DocMath, CorpusQA (≤128K / ≤1M)
General	MMLU-Pro, AIME 2024/2025, GPQA-Diamond
Memory	BFCL-V4 (memory subset), LongMemEval

🤝 Citation

@misc{lv2026golongrlcapabilityorientedlongcontext,
      title={GoLongRL: Capability-Oriented Long Context Reinforcement Learning with Multitask Alignment}, 
      author={Minxuan Lv and Tiehua Mei and Tanlong Du and Junmin Chen and Zhenpeng Su and Ziyang Chen and Ziqi Wang and Zhennan Wu and Ruotong Pan and jian Liang and Ruiming Tang and Han Li},
      year={2026},
      eprint={2605.19577},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2605.19577}, 
}