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Kwaipilot
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Model Details
Model Provider
Kwaipilot
Model Tree
Qwen/Qwen3-8B
Input Modalities
Output Modalities
Supported Functionality
This work is a companion to our earlier report HiPO: Hybrid Policy Optimization for Dynamic Reasoning in LLMs, where we first introduced the AutoThink paradigm for controllable reasoning. While KAT-V1 outlined the overall framework of SFT + RL for adaptive reasoning, this paper provides the detailed algorithmic design of that training recipe.
We introduce HiPO (Hybrid Policy Optimization for Dynamic Reasoning in LLMs), a novel RL framework designed to enable models to decide when to “think” (i.e., Think-on)and when to skip reasoning (i.e., Think-off), thereby striking a balance between correctness and efficiency.
HIPO has two main components:
Think-on Only (Overthinking).
Training only on Think-on data makes the model reason on all problems, causing inefficiency.
GRPO.
Improves accuracy by +3.1%, but increases token length on simple tasks.
Think-on/Think-off Mix.
Yields higher accuracy (+4.0%) while reducing token length (–10.8%) and thinking rate (–22%).
HiPO Advantage.
Achieves the best results: +6.2% accuracy, –30% token length, –39% thinking rate, outperforming existing methods in both efficiency and accuracy.
HiPO produces responses in a structured template that makes the reasoning path explicit and machine-parsable. Two modes are supported:
python
from transformers import AutoTokenizer, AutoModelForCausalLM model_name = "Kwaipilot/HiPO-8B" # load the tokenizer and the modeltokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)model = AutoModelForCausalLM.from_pretrained( model_name, torch_dtype="auto", device_map="auto") # prepare the model inputprompt = "Give me a short introduction to large language model."messages = [ {"role": "user", "content": prompt}]text = tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True)model_inputs = tokenizer([text], return_tensors="pt").to(model.device) # conduct text completiongenerated_ids = model.generate( **model_inputs, max_new_tokens=32768, temperature=0.6, top_p=0.95,)output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist() content = tokenizer.decode(output_ids, skip_special_tokens=True).strip("\n")print("prompt:\n", prompt)print("content:\n", content)text
@article{Zhan2025HiPO, title={HiPO: Hybrid Policy Optimization for Dynamic Reasoning in LLMs}, author={Ken Deng, Zizheng Zhan, Wen Xiang, Wenqiang Zhu and others}, year={2025}, institution={arXiv preprint arXiv:2509.23967}, number={arXiv:2509.23967}, url={https://arxiv.org/abs/2509.23967}}from transformers import AutoTokenizer, AutoModelForCausalLM model_name = "Kwaipilot/HiPO-8B" # load the tokenizer and the modeltokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)model = AutoModelForCausalLM.from_pretrained( model_name, torch_dtype="auto", device_map="auto") # prepare the model inputprompt = "Give me a short introduction to large language model."messages = [ {"role": "user", "content": prompt}]text = tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True)model_inputs = tokenizer([text], return_tensors="pt").to(model.device) # conduct text completiongenerated_ids = model.generate( **model_inputs, max_new_tokens=32768, temperature=0.6, top_p=0.95,)output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist() content = tokenizer.decode(output_ids, skip_special_tokens=True).strip("\n")print("prompt:\n", prompt)print("content:\n", content)@article{Zhan2025HiPO, title={HiPO: Hybrid Policy Optimization for Dynamic Reasoning in LLMs}, author={Ken Deng, Zizheng Zhan, Wen Xiang, Wenqiang Zhu and others}, year={2025}, institution={arXiv preprint arXiv:2509.23967}, number={arXiv:2509.23967}, url={https://arxiv.org/abs/2509.23967}}