license: apache-2.0
datasets:
- Tongyi-Zhiwen/DocQA-RL-1.6K
base_model:
- deepseek-ai/DeepSeek-R1-Distill-Qwen-32B
tags:
- long-context
- large-reasoning-model
QwenLong-L1: Towards Long-Context Large Reasoning Models with Reinforcement Learning
Fanqi Wan, Weizhou Shen, Shengyi Liao, Yingcheng Shi, Chenliang Li,
Ziyi Yang, Ji Zhang, Fei Huang, Jingren Zhou, Ming Yan
Tongyi Lab, Alibaba Group
🎉 News
May 26, 2025: 🔥 We release 🤗 QwenLong-L1-32B, which is the first long-context LRM trained with reinforcement learning for long-context reasoning. Experiments on seven long-context DocQA benchmarks demonstrate that QwenLong-L1-32B outperforms flagship LRMs like OpenAI-o3-mini and Qwen3-235B-A22B, achieving performance on par with Claude-3.7-Sonnet-Thinking, demonstrating leading performance among state-of-the-art LRMs.
May 26, 2025: 🔥 We release 🤗 DocQA-RL-1.6K, which is a specialized RL training dataset comprising 1.6K document question answering (DocQA) problems spanning mathematical, logical, and multi-hop reasoning domains.
📚 Introduction
In this work, we propose QwenLong-L1, a novel reinforcement learning (RL) framework designed to facilitate the transition of LRMs from short-context proficiency to robust long-context generalization. In our preliminary experiments, we illustrate the differences between the training dynamics of short-context and long-context reasoning RL.
Our framework enhances short-context LRMs through progressive context scaling during RL training. The framework comprises three core components: a warm-up supervised fine-tuning (SFT) phase to initialize a robust policy, a curriculum-guided RL phase that facilitates stable adaptation from short to long contexts, and a difficulty-aware retrospective sampling mechanism that adjusts training complexity across stages to incentivize policy exploration. Leveraging recent RL algorithms, including GRPO and DAPO, our framework integrates hybrid reward functions combining rule-based and model-based binary outcome rewards to balance precision and recall. Through strategic utilization of group relative advantages during policy optimization, it guides LRMs to learn effective reasoning patterns essential for robust long-context grounding and superior reasoning capabilities.
🎯 Model Release
We release 🤗 QwenLong-L1-32B, which is the first long-context LRM trained with reinforcement learniing for long-context reasoning. Experiments on seven long-context DocQA benchmarks demonstrate that QwenLong-L1-32B outperforms flagship LRMs like OpenAI-o3-mini and Qwen3-235B-A22B, achieving performance on par with Claude-3.7-Sonnet-Thinking, demonstrating leading performance among state-of-the-art LRMs.
Here are the evaluation results.
🛠️ Requirements
# Create the conda environment
conda create -n qwenlongl1 python==3.10
conda activate qwenlongl1
# Install requirements
pip3 install -r requirements.txt
# Install verl
cd verl
pip3 install -e .
# Install vLLM
pip3 install vllm==0.7.3
# Install flash-attn
pip3 install flash-attn --no-build-isolation
🚀 Quick Start
Here's how you can run the model using the 🤗 Transformers:
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "Tongyi-Zhiwen/QwenLong-L1-32B"
# load the tokenizer and the model
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype="auto",
device_map="auto"
)
# prepare the model input
template = """Please read the following text and answer the question below.
<text>
$DOC$
</text>
$Q$
Format your response as follows: "Therefore, the answer is (insert answer here)"."""
context = "<YOUR_CONTEXT_HERE>"
question = "<YOUR_QUESTION_HERE>"
prompt = template.replace('$DOC$', context.strip()).replace('$Q$', question.strip())
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# conduct text completion
generated_ids = model.generate(
**model_inputs,
max_new_tokens=10000,
temperature=0.7,
top_p=0.95
)
output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist()
# parsing thinking content
try:
# rindex finding 151649 (</think>)
index = len(output_ids) - output_ids[::-1].index(151649)
except ValueError:
index = 0
thinking_content = tokenizer.decode(output_ids[:index], skip_special_tokens=True).strip("\n")
content = tokenizer.decode(output_ids[index:], skip_special_tokens=True).strip("\n")
print("thinking content:", thinking_content)
print("content:", content)
🗂️ Dataset
To construct a challenging RL dataset for verifiable long-context reasoning, we develop 🤗 DocQA-RL-1.6K, which comprises 1.6K DocQA problems across three reasoning domains:
(1) Mathematical Reasoning: We use 600 problems from the DocMath dataset, requiring numerical reasoning across long and specialized documents such as financial reports. For DocMath, we sample 75% items from each subset from its valid split for training and 25% for evaluation;
(2) Logical Reasoning: We employ DeepSeek-R1 to synthesize 600 multi-choice questions requiring logic analysis of real-world documents spanning legal, financial, insurance, and production domains from our curated collection;
(3) Multi-Hop Reasoning: We sample 200 examples from MultiHopRAG and 200 examples from Musique, emphasizing cross-document reasoning.
Please download and put the following datasets in ./datasets/ for training and evaluation.
RL training data: 🤗 DocQA-RL-1.6K.
Evaluation data: 🤗 docmath, 🤗 frames, 🤗 longbench.
💻 Training
We provide the basic demo training code for single stage RL traininig with DAPO.
First, we should start a local verifier.
export CUDA_VISIBLE_DEVICES=0
vllm serve "Qwen/Qwen2.5-1.5B-Instruct" \
--host 0.0.0.0 \
--port 23547
Then, we start RL training with 4 nodes.
export PROJ_DIR="<YOUR_PROJ_DIR_HERE>"
export MASTER_IP="<YOUR_MASTER_IP_HERE>" # ray master ip
export NNODES=4 # total GPU nodes
export NODE_RANK=${RANK} # rank of current node
export PORT=6382
export WANDB_API_KEY="<YOUR_WANDB_API_KEY_HERE>"
export WANDB_PROJECT="QwenLong-L1"
export LLM_JUDGE=Y # 'Y': LLM JUDGE, 'N': RULE BASED
export VLLM_ATTENTION_BACKEND=FLASH_ATTN
# verifier
export VERIFIER_PATH="Qwen/Qwen2.5-1.5B-Instruct"
export VERIFIER_HOST="<YOUR_VERIFIER_HOST_HERE>"
export VERIFIER_PORT="23547"
ray_start_retry() {
while true; do
ray start --address="${MASTER_IP}:${PORT}"
if [ $? -eq 0 ]; then
break
fi
echo "Failed to connect to master, retrying in 5 seconds..."
sleep 5
done
}
check_ray_status() {
until ray status >/dev/null 2>&1; do
echo "Waiting for Ray cluster to be ready..."
sleep 5
done
}
if [ "$RANK" == "0" ]; then
echo "Starting HEAD node..."
ray start --head --port=${PORT}
check_ray_status
echo "Ray head node started successfully"
else
echo "Starting WORKER node..."
ray_start_retry
check_ray_status
echo "Successfully joined Ray cluster"
fi
if [ "$RANK" == "0" ]; then
bash ${PROJ_DIR}/scripts/rl_4nodes_dapo.sh 2>&1 | tee ${PROJ_DIR}/logs/rl_log_$(date +%Y%m%d_%H%M%S).txt &
else
sleep 30d
fi
wait
📊 Evaluation
We conduct evaluation on seven long-context DocQA benchmarks, including multi-hop reasoning benchmarks such as 2WikiMultihopQA, HotpotQA, Musique, NarrativeQA, Qasper, and Frames as well as mathematical reasoning benchmarks like DocMath. We report the maximum of exact match and LLM-judged accuracy as the final score, aligned with the reward function in our RL training process. We use DeepSeek-V3 as the judge model with a temperature of 0.0 to provide a reliable evaluation.
# Step 1. Serve the model for evaluation
export CUDA_VISIBLE_DEVICES="0,1,2,3,4,5,6,7"
MODEL_NAME="QwenLong-L1-32B"
MODEL_PATH="Tongyi-Zhiwen/QwenLong-L1-32B"
vllm serve ${MODEL_PATH} \
--port 23547 \
--api-key "token-abc123" \
--tensor-parallel-size 8 \
--gpu-memory-utilization 0.95 \
--max_model_len 131072 \
--trust-remote-code
# Step 2. Generate model responses for each dataset
export SERVE_HOST="<YOUR_SERVE_HOST_HERE>" # e.g., 127.0.0.1
export SERVE_PORT="23547"
PROJ_DIR="<YOUR_PROJ_DIR_HERE>"
DATA="<YOUR_DATA_HERE>" # e.g., docmath, frames, 2wikimqa, hotpotqa, musique, narrativeqa, pasper
python ${PROJ_DIR}/eval/${DATA}.py \
--save_dir "${PROJ_DIR}/eval/results/${DATA}" \
--save_file "${MODEL_NAME}" \
--model "${MODEL_PATH}" \
--tokenizer "${MODEL_PATH}" \
--n_proc 16 \
--api "openai"
# Step 3. Verify model responses for each dataset
export VERIFIER_API="<YOUR_API_KEY_HERE>"
export VERIFIER_URL="https://api.deepseek.com/v1"
PROJ_DIR="<YOUR_PROJ_DIR_HERE>"
DATA="<YOUR_DATA_HERE>" # e.g., docmath, frames, 2wikimqa, hotpotqa, musique, narrativeqa, pasper
python ${PROJ_DIR}/eval/${DATA}_verify.py \
--save_dir "${PROJ_DIR}/results/${DATA}" \
--save_file "${MODEL_NAME}" \
--judge_model "deepseek-chat" \
--batch_size 20
📝 Citation
If you find this work is relevant with your research or applications, please feel free to cite our work!
@article{wan2025qwenlongl1,
title={QwenLong-L1: : Towards Long-Context Large Reasoning Models with Reinforcement Learning},
author={Fanqi Wan, Weizhou Shen, Shengyi Liao, Yingcheng Shi, Chenliang Li, Ziyi Yang, Ji Zhang, Fei Huang, Jingren Zhou, Ming Yan},
journal={arXiv preprint arXiv:2505.17667},
year={2025}
}