--- 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 ----------------------------- [![License](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](https://opensource.org/licenses/Apache-2.0) [![arXiv](https://img.shields.io/badge/arXiv-xxxx.xxxxx-b31b1b.svg)](https://arxiv.org/abs/xxxx.xxxxx) [![GitHub](https://img.shields.io/badge/GitHub-TongyiZhiwen-4b32c3?logo=github)](https://github.com/Tongyi-Zhiwen) [![ModelScope](https://img.shields.io/badge/🤖%20ModelScope-purple)](https://modelscope.cn/organization/iic/) [![HuggingFace](https://img.shields.io/badge/🤗%20HuggingFace-yellow)](https://huggingface.co/Tongyi-Zhiwen) _**Fanqi Wan, Weizhou Shen, Shengyi Liao, Yingcheng Shi, Chenliang Li, Ziyi Yang, Ji Zhang, Fei Huang, Jingren Zhou, Ming Yan**_ _Qwen-Doc Team, Alibaba Group_


## 🎉 News - **May 26, 2025:** 🔥 We release [🤗 QwenLong-L1-32B](https://huggingface.co/Tongyi-Zhiwen/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. - **May 26, 2025:** 🔥 We release [🤗 DocQA-RL-1.6K](https://huggingface.co/datasets/Tongyi-Zhiwen/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](https://huggingface.co/Tongyi-Zhiwen/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 ```bash # 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: ```python 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. $DOC$ $Q$ Format your response as follows: "Therefore, the answer is (insert answer here)".""" context = "" question = "" 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 () 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](https://huggingface.co/datasets/Tongyi-Zhiwen/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](https://huggingface.co/datasets/Tongyi-Zhiwen/DocQA-RL-1.6K). Evaluation data: [🤗 docmath](https://huggingface.co/datasets/Tongyi-Zhiwen/docmath), [🤗 frames](https://huggingface.co/datasets/Tongyi-Zhiwen/frames), [🤗 longbench](https://huggingface.co/datasets/Tongyi-Zhiwen/longbench). ## 💻 Training We provide the basic demo training code for single stage RL traininig with DAPO. First, we should start a local verifier. ```bash 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. ```bash export PROJ_DIR="" export MASTER_IP="" # ray master ip export NNODES=4 # total GPU nodes export NODE_RANK=${RANK} # rank of current node export PORT=6382 export WANDB_API_KEY="" export WANDB_PROJECT="QwenLong-L1" export LLM_JUDGE=Y # 'Y': LLM JUDGE, 'N': RULE BASED export VLLM_ATTENTION_BACKEND=FLASH_ATTN # verifier export VERIFIER_PATH="Qwen/Qwen2.5-1.5B-Instruct" export VERIFIER_HOST="" export VERIFIER_PORT="23547" ray_start_retry() { while true; do ray start --address="${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. ```bash # 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="" # e.g., 127.0.0.1 export SERVE_PORT="23547" PROJ_DIR="" DATA="" # 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="" export VERIFIER_URL="https://api.deepseek.com/v1" PROJ_DIR="" DATA="" # 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:xxxx.xxxxx}, year={2025} } ```