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  2. README.md +366 -3
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  *.zst filter=lfs diff=lfs merge=lfs -text
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README.md CHANGED
@@ -1,3 +1,366 @@
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- ---
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- license: apache-2.0
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- ---
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
2
+ license: apache-2.0
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+ base_model:
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+ - Tongyi-Zhiwen/QwenLong-L1-32B
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+ tags:
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+ - long-context
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+ - large-reasoning-model
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+ pipeline_tag: text-generation
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+ library_name: exllamav2
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+ ---
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+
12
+ ## Quantization
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+
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+ ```
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+ python ./exllamav2/convert.py \
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+ -i /models/Tongyi-Zhiwen/QwenLong-L1-32B \
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+ -o /models/Tongyi-Zhiwen/QwenLong-L1-32B-4.25bpw \
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+ -cf /models/Tongyi-Zhiwen/QwenLong-L1-32B-4.25bpw \
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+ -b 4.25 -hb 8 -ss 0
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+ ```
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+
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+ ---
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+
24
+ # QwenLong-L1: Towards Long-Context Large Reasoning Models with Reinforcement Learning
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+
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+ <p align="center" width="100%">
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+ </p>
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+
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+ <div id="top" align="center">
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+
31
+ ---
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+
33
+ [![License](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](https://opensource.org/licenses/Apache-2.0)
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+ [![arXiv](https://img.shields.io/badge/arXiv-2505.17667-b31b1b.svg)](https://arxiv.org/abs/2505.17667)
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+ [![GitHub](https://img.shields.io/badge/GitHub-QwenLongL1-4b32c3?logo=github)](https://github.com/Tongyi-Zhiwen/QwenLong-L1)
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+ [![ModelScope](https://img.shields.io/badge/🤖%20ModelScope-purple)](https://modelscope.cn/models/iic/QwenLong-L1-32B)
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+ [![HuggingFace](https://img.shields.io/badge/🤗%20HuggingFace-yellow)](https://huggingface.co/Tongyi-Zhiwen/QwenLong-L1-32B)
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+
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+ <!-- **Authors:** -->
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+
41
+ _**Fanqi Wan, Weizhou Shen, Shengyi Liao, Yingcheng Shi, Chenliang Li,**_
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+
43
+ _**Ziyi Yang, Ji Zhang, Fei Huang, Jingren Zhou, Ming Yan**_
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+
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+ <!-- **Affiliations:** -->
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+
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+ _Tongyi Lab, Alibaba Group_
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+
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+ <p align="center">
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+ <img src="./assets/fig1.png" width="100%"> <br>
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+ </p>
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+
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+ </div>
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+
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+ ## 🎉 News
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+
57
+ - **May 28, 2025:** 🔥 We release [🤗 QwenLong-L1-32B-AWQ](https://huggingface.co/Tongyi-Zhiwen/QwenLong-L1-32B-AWQ), which has undergone AWQ int4 quantization using the ms-swift framework.
58
+
59
+ - **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 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.
60
+
61
+ - **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.
62
+
63
+ ## 📚 Introduction
64
+
65
+ 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.
66
+
67
+ <p align="center">
68
+ <img src="./assets/fig2.png" width="100%"> <br>
69
+ </p>
70
+
71
+ 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.
72
+
73
+ <p align="center">
74
+ <img src="./assets/fig3.png" width="100%"> <br>
75
+ </p>
76
+
77
+ ## 🎯 Model Release
78
+
79
+ 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.
80
+
81
+ Here are the evaluation results.
82
+
83
+ <p align="center">
84
+ <img src="./assets/tab4.png" width="100%"> <br>
85
+ </p>
86
+
87
+ ## 🛠️ Requirements
88
+
89
+ ```bash
90
+ # Create the conda environment
91
+ conda create -n qwenlongl1 python==3.10
92
+ conda activate qwenlongl1
93
+
94
+ # Install requirements
95
+ pip3 install -r requirements.txt
96
+
97
+ # Install verl
98
+ cd verl
99
+ pip3 install -e .
100
+
101
+ # Install vLLM
102
+ pip3 install vllm==0.7.3
103
+
104
+ # Install flash-attn
105
+ pip3 install flash-attn --no-build-isolation
106
+ ```
107
+
108
+ ## 🚀 Quick Start
109
+
110
+ Here's how you can run the model using the 🤗 Transformers:
111
+
112
+ ```python
113
+ from transformers import AutoModelForCausalLM, AutoTokenizer
114
+
115
+ model_name = "Tongyi-Zhiwen/QwenLong-L1-32B"
116
+
117
+ # load the tokenizer and the model
118
+ tokenizer = AutoTokenizer.from_pretrained(model_name)
119
+ model = AutoModelForCausalLM.from_pretrained(
120
+ model_name,
121
+ torch_dtype="auto",
122
+ device_map="auto"
123
+ )
124
+
125
+ # prepare the model input
126
+ template = """Please read the following text and answer the question below.
127
+
128
+ <text>
129
+ $DOC$
130
+ </text>
131
+
132
+ $Q$
133
+
134
+ Format your response as follows: "Therefore, the answer is (insert answer here)"."""
135
+ context = "<YOUR_CONTEXT_HERE>"
136
+ question = "<YOUR_QUESTION_HERE>"
137
+ prompt = template.replace('$DOC$', context.strip()).replace('$Q$', question.strip())
138
+ messages = [
139
+ # {"role": "system", "content": "You are QwenLong-L1, created by Alibaba Tongyi Lab. You are a helpful assistant."}, # Use system prompt to define identity when needed.
140
+ {"role": "user", "content": prompt}
141
+ ]
142
+ text = tokenizer.apply_chat_template(
143
+ messages,
144
+ tokenize=False,
145
+ add_generation_prompt=True
146
+ )
147
+ model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
148
+
149
+ # conduct text completion
150
+ generated_ids = model.generate(
151
+ **model_inputs,
152
+ max_new_tokens=10000,
153
+ temperature=0.7,
154
+ top_p=0.95
155
+ )
156
+ output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist()
157
+
158
+ # parsing thinking content
159
+ try:
160
+ # rindex finding 151649 (</think>)
161
+ index = len(output_ids) - output_ids[::-1].index(151649)
162
+ except ValueError:
163
+ index = 0
164
+
165
+ thinking_content = tokenizer.decode(output_ids[:index], skip_special_tokens=True).strip("\n")
166
+ content = tokenizer.decode(output_ids[index:], skip_special_tokens=True).strip("\n")
167
+
168
+ print("thinking content:", thinking_content)
169
+ print("content:", content)
170
+ ```
171
+
172
+ ## ♾️ Processing Long Documents
173
+
174
+ For input where the total length (including both input and output) significantly exceeds 32,768 tokens, we recommend using RoPE scaling techniques to handle long texts effectively. We have validated the model's performance on context lengths of up to 131,072 tokens using the [YaRN](https://arxiv.org/abs/2309.00071) method.
175
+
176
+ YaRN is currently supported by several inference frameworks, e.g., `transformers` and `llama.cpp` for local use, `vllm` and `sglang` for deployment. In general, there are two approaches to enabling YaRN for supported frameworks:
177
+
178
+ - Modifying the model files:
179
+ In the `config.json` file, add the `rope_scaling` fields:
180
+ ```json
181
+ {
182
+ ...,
183
+ "rope_scaling": {
184
+ "rope_type": "yarn",
185
+ "factor": 4.0,
186
+ "original_max_position_embeddings": 32768
187
+ }
188
+ }
189
+ ```
190
+ For `llama.cpp`, you need to regenerate the GGUF file after the modification.
191
+ - Passing command line arguments:
192
+
193
+ For `vllm`, you can use
194
+ `shell
195
+ vllm serve ... --rope-scaling '{"rope_type":"yarn","factor":4.0,"original_max_position_embeddings":32768}' --max-model-len 131072
196
+ `
197
+ For `sglang`, you can use
198
+ `shell
199
+ python -m sglang.launch_server ... --json-model-override-args '{"rope_scaling":{"rope_type":"yarn","factor":4.0,"original_max_position_embeddings":32768}}'
200
+ `
201
+ For `llama-server` from `llama.cpp`, you can use
202
+ `shell
203
+ llama-server ... --rope-scaling yarn --rope-scale 4 --yarn-orig-ctx 32768
204
+ `
205
+
206
+ > [!IMPORTANT]
207
+ > If you encounter the following warning
208
+ >
209
+ > ```
210
+ > Unrecognized keys in `rope_scaling` for 'rope_type'='yarn': {'original_max_position_embeddings'}
211
+ > ```
212
+ >
213
+ > please upgrade `transformers>=4.51.0`.
214
+
215
+ > [!NOTE]
216
+ > All the notable open-source frameworks implement static YaRN, which means the scaling factor remains constant regardless of input length, **potentially impacting performance on shorter texts.**
217
+ > We advise adding the `rope_scaling` configuration only when processing long contexts is required.
218
+ > It is also recommended to modify the `factor` as needed. For example, if the typical context length for your application is 65,536 tokens, it would be better to set `factor` as 2.0.
219
+
220
+ > [!NOTE]
221
+ > If the average context length does not exceed 32,768 tokens, we do not recommend enabling YaRN in this scenario, as it may potentially degrade model performance.
222
+
223
+ ## 🗂️ Dataset
224
+
225
+ 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:
226
+
227
+ (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;
228
+
229
+ (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;
230
+
231
+ (3) Multi-Hop Reasoning: We sample 200 examples from MultiHopRAG and 200 examples from Musique, emphasizing cross-document reasoning.
232
+
233
+ Please download and put the following datasets in `./datasets/` for training and evaluation.
234
+
235
+ RL training data: [🤗 DocQA-RL-1.6K](https://huggingface.co/datasets/Tongyi-Zhiwen/DocQA-RL-1.6K).
236
+
237
+ 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).
238
+
239
+ ## 💻 Training
240
+
241
+ We provide the basic demo training code for single stage RL traininig with DAPO.
242
+
243
+ First, we should start a local verifier.
244
+
245
+ ```bash
246
+ export CUDA_VISIBLE_DEVICES=0
247
+
248
+ vllm serve "Qwen/Qwen2.5-1.5B-Instruct" \
249
+ --host 0.0.0.0 \
250
+ --port 23547
251
+ ```
252
+
253
+ Then, we start RL training with 4 nodes.
254
+
255
+ ```bash
256
+ export PROJ_DIR="<YOUR_PROJ_DIR_HERE>"
257
+ export MASTER_IP="<YOUR_MASTER_IP_HERE>" # ray master ip
258
+ export NNODES=4 # total GPU nodes
259
+ export NODE_RANK=${RANK} # rank of current node
260
+ export PORT=6382
261
+ export WANDB_API_KEY="<YOUR_WANDB_API_KEY_HERE>"
262
+ export WANDB_PROJECT="QwenLong-L1"
263
+ export LLM_JUDGE=Y # 'Y': LLM JUDGE, 'N': RULE BASED
264
+ export VLLM_ATTENTION_BACKEND=FLASH_ATTN
265
+ # verifier
266
+ export VERIFIER_PATH="Qwen/Qwen2.5-1.5B-Instruct"
267
+ export VERIFIER_HOST="<YOUR_VERIFIER_HOST_HERE>"
268
+ export VERIFIER_PORT="23547"
269
+
270
+ ray_start_retry() {
271
+ while true; do
272
+ ray start --address="${MASTER_IP}:${PORT}"
273
+ if [ $? -eq 0 ]; then
274
+ break
275
+ fi
276
+ echo "Failed to connect to master, retrying in 5 seconds..."
277
+ sleep 5
278
+ done
279
+ }
280
+
281
+ check_ray_status() {
282
+ until ray status >/dev/null 2>&1; do
283
+ echo "Waiting for Ray cluster to be ready..."
284
+ sleep 5
285
+ done
286
+ }
287
+
288
+ if [ "$RANK" == "0" ]; then
289
+ echo "Starting HEAD node..."
290
+ ray start --head --port=${PORT}
291
+
292
+ check_ray_status
293
+ echo "Ray head node started successfully"
294
+
295
+ else
296
+ echo "Starting WORKER node..."
297
+ ray_start_retry
298
+
299
+ check_ray_status
300
+ echo "Successfully joined Ray cluster"
301
+ fi
302
+
303
+ if [ "$RANK" == "0" ]; then
304
+ bash ${PROJ_DIR}/scripts/rl_4nodes_dapo.sh 2>&1 | tee ${PROJ_DIR}/logs/rl_log_$(date +%Y%m%d_%H%M%S).txt &
305
+ else
306
+ sleep 30d
307
+ fi
308
+
309
+ wait
310
+ ```
311
+
312
+ ## 📊 Evaluation
313
+
314
+ 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.
315
+
316
+ ```bash
317
+ # Step 1. Serve the model for evaluation
318
+ export CUDA_VISIBLE_DEVICES="0,1,2,3,4,5,6,7"
319
+ MODEL_NAME="QwenLong-L1-32B"
320
+ MODEL_PATH="Tongyi-Zhiwen/QwenLong-L1-32B"
321
+
322
+ vllm serve ${MODEL_PATH} \
323
+ --port 23547 \
324
+ --api-key "token-abc123" \
325
+ --tensor-parallel-size 8 \
326
+ --gpu-memory-utilization 0.95 \
327
+ --max_model_len 131072 \
328
+ --trust-remote-code
329
+
330
+ # Step 2. Generate model responses for each dataset
331
+ export SERVE_HOST="<YOUR_SERVE_HOST_HERE>" # e.g., 127.0.0.1
332
+ export SERVE_PORT="23547"
333
+ PROJ_DIR="<YOUR_PROJ_DIR_HERE>"
334
+ DATA="<YOUR_DATA_HERE>" # e.g., docmath, frames, 2wikimqa, hotpotqa, musique, narrativeqa, pasper
335
+ python ${PROJ_DIR}/eval/${DATA}.py \
336
+ --save_dir "${PROJ_DIR}/eval/results/${DATA}" \
337
+ --save_file "${MODEL_NAME}" \
338
+ --model "${MODEL_PATH}" \
339
+ --tokenizer "${MODEL_PATH}" \
340
+ --n_proc 16 \
341
+ --api "openai"
342
+
343
+ # Step 3. Verify model responses for each dataset
344
+ export VERIFIER_API="<YOUR_API_KEY_HERE>"
345
+ export VERIFIER_URL="https://api.deepseek.com/v1"
346
+ PROJ_DIR="<YOUR_PROJ_DIR_HERE>"
347
+ DATA="<YOUR_DATA_HERE>" # e.g., docmath, frames, 2wikimqa, hotpotqa, musique, narrativeqa, pasper
348
+ python ${PROJ_DIR}/eval/${DATA}_verify.py \
349
+ --save_dir "${PROJ_DIR}/results/${DATA}" \
350
+ --save_file "${MODEL_NAME}" \
351
+ --judge_model "deepseek-chat" \
352
+ --batch_size 20
353
+ ```
354
+
355
+ ## 📝 Citation
356
+
357
+ If you find this work is relevant with your research or applications, please feel free to cite our work!
358
+
359
+ ```
360
+ @article{wan2025qwenlongl1,
361
+ title={QwenLong-L1: : Towards Long-Context Large Reasoning Models with Reinforcement Learning},
362
+ author={Fanqi Wan, Weizhou Shen, Shengyi Liao, Yingcheng Shi, Chenliang Li, Ziyi Yang, Ji Zhang, Fei Huang, Jingren Zhou, Ming Yan},
363
+ journal={arXiv preprint arXiv:2505.17667},
364
+ year={2025}
365
+ }
366
+ ```
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