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[NeurIPS 2025] Thinkless: LLM Learns When to Think
| Date | Stars |
|---|---|
| 2026-07-31 | 261 |
| 2026-09-05 | 261 |
| 2026-09-14 | 260 |
| 2026-09-19 | 259 |
| 2026-09-20 | 259 |
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# Thinkless: LLM Learns When to Think
> [**Thinkless: LLM Learns When to Think**](http://arxiv.org/abs/2505.13379)
> *[Gongfan Fang](https://fangggf.github.io/), [Xinyin Ma](https://horseee.github.io/), [Xinchao Wang](https://sites.google.com/site/sitexinchaowang/)*
> *[xML Lab](https://sites.google.com/view/xml-nus), National University of Singapore*
<table>
<table>
<thead>
</thead>
<tbody>
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<td>📄 <strong>Paper Link</strong></td>
<td><a href="http://arxiv.org/abs/2505.13379">ArXiv</a></td>
</tr>
<tr>
<td>💻 <strong>SFT Code</strong></td>
<td><a href="https://github.com/VainF/Reasoning-SFT">VainF/Reasoning-SFT</a></td>
</tr>
<tr>
<td>🤖 <strong>RL Model</strong></td>
<td><a href="https://huggingface.co/Vinnnf/Thinkless-1.5B-RL-DeepScaleR">Thinkless-1.5B-RL-DeepScaleR</a></td>
</tr>
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<td>🐣 <strong>Warmup Model</strong></td>
<td><a href="https://huggingface.co/Vinnnf/Thinkless-1.5B-Warmup">Thinkless-1.5B-Warmup</a></td>
</tr>
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<td>📊 <strong>Data for Warmup</strong></td>
<td><a href="https://huggingface.co/datasets/Vinnnf/Hybrid-OpenThoughts2-1M-1.5B">Hybrid-OpenThoughts2-1M-1.5B</a></td>
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<td>📊 <strong>Data for RL</strong></td>
<td><a href="https://huggingface.co/datasets/agentica-org/DeepScaleR-Preview-Dataset">agentica-org/DeepScaleR-Preview-Dataset</a></td>
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## Introduction
> ***Can LLMs learn when to think?***
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 improving the computational efficiency of Reasoning Language Models.
## The Full Pipeline
## Installation
```bash
conda create -n thinkless python==3.10
conda activate thinkless
# For training
cd Thinkless
pip install torch==2.4.0 lm_eval==0.4.8 ray==2.45.0 # install lm_eval before verl to avoid conflict
pip install -e ./verl
pip install -e .
# https://github.com/vllm-project/vllm/issues/4392
pip install nvidia-cublas-cu12==12.4.5.8
```
## Quick Start
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "Vinnnf/Thinkless-1.5B-RL-DeepScaleR"
model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained(model_name)
instruction = "Please reason step by step, and put your final answer within \\boxed{}."
prompt = "The arithmetic mean of 7, 2, $x$ and 10 is 9. What is the value of $x$?"
#prompt = "What is the smallest positive perfect cube that can be written as the sum of three consecutive integers?"
# prompt = "How many r's are in the word \"strawberry\""
messages = [
{"role": "user", "content": f"{instruction}\n{prompt}"},
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], reExcerpt of 17,595 characters
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