DECS_1.5B / README.md

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base_model: deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B
language:
  - zh
  - en
license: other
pipeline_tag: text-generation
library_name: transformers
tags:
  - deepscaler
  - reasoning
  - grpo
  - qwen2

DECS_1.5B

This is the official model for the ICLR 2026 Oral paper: "Overthinking Reduction with Decoupled Rewards and Curriculum Data Scheduling".

Paper | Code | Project Page

DECS_1.5B is a reasoning-focused causal language model built from deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B and further trained with the DECS algorithm, focused on 50% fewer tokens when answering a reasoning-required problem.

Model Summary

Base model: deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B
Upload date: 2026-02-24
Recommended use: long-form reasoning and mathematical/problem-solving style generation

Quick Start (Transformers)

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "pixas/DECS_1.5B"
tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.bfloat16,
    device_map="auto",
)

messages = [
    {"role": "user", "content": "Solve: If x^2 - 5x + 6 = 0, what are x values?"}
]
prompt = tokenizer.apply_chat_template(
    messages, tokenize=False, add_generation_prompt=True
)
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)

with torch.no_grad():
    outputs = model.generate(
        **inputs,
        max_new_tokens=512,
        temperature=0.6,
        top_p=0.95,
    )

new_tokens = outputs[0][inputs["input_ids"].shape[-1]:]
print(tokenizer.decode(new_tokens, skip_special_tokens=True))

Quick Start (vLLM)

from vllm import LLM, SamplingParams

llm = LLM(model="pixas/DECS_1.5B", trust_remote_code=True)
sampling = SamplingParams(temperature=0.6, top_p=0.95, max_tokens=512)
prompt = "Please reason step by step: what is 37 * 48?"
outputs = llm.generate([prompt], sampling_params=sampling)
print(outputs[0].outputs[0].text)

Notes

This model may produce incorrect or unverifiable reasoning. Always validate outputs in high-stakes settings.
Performance can vary by prompt style and decoding parameters.
License and acceptable-use constraints should follow the upstream base model and your deployment policy.

Citation

If you use this model, please cite our paper:

@inproceedings{jiang2026overthinking,
title={Overthinking Reduction with Decoupled Rewards and Curriculum Data Scheduling},
author={Shuyang Jiang and Yusheng Liao and Ya Zhang and Yanfeng Wang and Yu Wang},
booktitle={The Fourteenth International Conference on Learning Representations},
year={2026},
url={https://openreview.net/forum?id=kdeiRledV6}
}