Thinkless 1.5B Warmup

🚀 Thinkless: LLM Learns When to Think

Thinkless is a learnable framework that enables an LLM to adaptively choose between short - form and long - form reasoning. It reduces the computational cost of Reasoning Language Models by decreasing long - chain thinking usage on benchmarks.

🚀 Quick Start

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "Vinnnf/Thinkless-1.5B-Warmup"

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 = f"{instruction}\nThe arithmetic mean of 7, 2, $x$ and 10 is 9. What is the value of $x$?"

messages = [
    {"role": "user", "content": prompt}
]

text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)

think_mode = True
if think_mode:
    text = f"{text}<think>"
else:
    text = f"{text}<short>"

model_inputs = tokenizer([text], return_tensors="pt").to(model.device)

generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=4096
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
num_tokens = len(generated_ids[0])

response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]

print(text+response)
print(f"\nThink Mode: {think_mode}")
print(f"Number of tokens: {num_tokens}")

✨ Features

[!NOTE] 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 reducing the computational cost of Reasoning Language Models.

📚 Documentation

Pipeline

📄 License

This project is licensed under the Apache - 2.0 license.

📚 Citation

If you find this work helpful, please cite:

@article{fang2025thinkless,
  title={Thinkless: LLM Learns When to Think},
  author={Fang, Gongfan and Ma, Xinyin and Wang, Xinchao},
  journal={arXiv preprint arXiv:2505.13379},
  year={2025}
}