126 lines
4.3 KiB
Markdown
126 lines
4.3 KiB
Markdown
---
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license: apache-2.0
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datasets:
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- open-thoughts/OpenThoughts2-1M
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- Vinnnf/Hybrid-OpenThoughts2-1M-1.5B
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base_model:
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- deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B
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pipeline_tag: text-generation
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library_name: transformers
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---
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# Thinkless: LLM Learns When to Think
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<table>
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<thead>
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</thead>
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<tbody>
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<tr>
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<td>📄 <strong>Paper Link</strong></td>
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<td><a href="https://arxiv.org/abs/2505.13379">ArXiv</a></td>
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</tr>
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<tr>
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<td>💻 <strong>RL Code</strong></td>
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<td><a href="https://github.com/VainF/Thinkless">VainF/Thinkless</a></td>
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</tr>
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<tr>
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<td>💻 <strong>SFT Code</strong></td>
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<td><a href="https://github.com/VainF/Reasoning-SFT">VainF/Reasoning-SFT</a></td>
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</tr>
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<tr>
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<td>🤖 <strong>RL Model</strong></td>
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<td><a href="https://huggingface.co/Vinnnf/Thinkless-1.5B-RL-DeepScaleR">Thinkless-1.5B-RL-DeepScaleR</a></td>
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</tr>
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<tr>
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<td>🐣 <strong>Warmup Model</strong></td>
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<td><a href="https://huggingface.co/Vinnnf/Thinkless-1.5B-Warmup">Thinkless-1.5B-Warmup</a></td>
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</tr>
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<tr>
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<td>📊 <strong>Data for Warmup</strong></td>
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<td><a href="https://huggingface.co/datasets/Vinnnf/Hybrid-OpenThoughts2-1M-1.5B">Hybrid-OpenThoughts2-1M-1.5B</a></td>
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</tr>
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<tr>
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<td>📊 <strong>Data for RL</strong></td>
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<td><a href="https://huggingface.co/datasets/agentica-org/DeepScaleR-Preview-Dataset">agentica-org/DeepScaleR-Preview-Dataset</a></td>
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</tr>
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</tbody>
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</table>
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## Introduction
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> [!NOTE]
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> ***Can LLMs learn when to think?***
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We propose Thinkless, a learnable framework that empowers an LLM to adaptively select between short-form and long-form reasoning based on both task complexity and the model's ability. Thinkless is trained under a reinforcement learning paradigm and employs two control tokens, \<short\> for concise responses and \<think\> for detailed reasoning. At the core of our method is a Decoupled Group Relative Policy Optimization (DeGRPO) algorithm, which decomposes the learning objective of hybrid reasoning into two components: (1) a control token loss that governs the selection of the reasoning mode, and (2) a response loss that improves the accuracy of the generated answers. This decoupled formulation enables fine-grained control over the contributions of each objective, stabilizing training and effectively preventing collapse observed in vanilla GRPO. Empirically, on several benchmarks such as Minerva Algebra, MATH-500, and GSM8K, Thinkless is able to reduce the usage of long-chain thinking by 50\% - 90\%, significantly reducing the computational cost of Reasoning Language Models.
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## Pipeline
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## QuickStart
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model_name = "Vinnnf/Thinkless-1.5B-Warmup"
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model = AutoModelForCausalLM.from_pretrained(
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model_name,
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torch_dtype="auto",
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device_map="auto"
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)
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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instruction = "Please reason step by step, and put your final answer within \\boxed{}."
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prompt = f"{instruction}\nThe arithmetic mean of 7, 2, $x$ and 10 is 9. What is the value of $x$?"
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messages = [
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{"role": "user", "content": prompt}
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]
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text = tokenizer.apply_chat_template(
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messages,
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tokenize=False,
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add_generation_prompt=True
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)
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think_mode = True
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if think_mode:
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text = f"{text}<think>"
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else:
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text = f"{text}<short>"
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model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
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generated_ids = model.generate(
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**model_inputs,
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max_new_tokens=4096
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)
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generated_ids = [
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output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
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]
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num_tokens = len(generated_ids[0])
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response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
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print(text+response)
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print(f"\nThink Mode: {think_mode}")
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print(f"Number of tokens: {num_tokens}")
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```
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## Citation
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If you find this work helpful, please cite:
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```
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@article{fang2025thinkless,
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title={Thinkless: LLM Learns When to Think},
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author={Fang, Gongfan and Ma, Xinyin and Wang, Xinchao},
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journal={arXiv preprint arXiv:2505.13379},
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year={2025}
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}
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``` |