ZJU-REAL/Relay-OPD

Última atividade de origem registrada: 28 de julho de 2026 às 19:08

Catálogo do SkillsMP atualizado: 2 de novembro de 2026 às 18:02

skills coletadas: 1

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<div align="center"> # <img src="docs/assets/logo.png" alt="Relay-OPD" height="40"> Pass the Baton: Trajectory-Relayed On-Policy Distillation [![arXiv](https://img.shields.io/badge/Paper-arxiv%3A2607.26057-b31b1b)](https://arxiv.org/abs/2607.26057) [![Daily Paper](https://img.shields.io/badge/Daily%20Paper-huggingface-yellow)](https://huggingface.co/papers/2607.26057) [![Project Page](https://img.shields.io/badge/Project-Page-blue)](https://zju-real.github.io/Relay-OPD/) [![License](https://img.shields.io/badge/License-Apache_2.0-green)](relay-opd/LICENSE) Haolei Xu1,2* · Xiaowen Xu2* · Haiwen Hong2*&dagger; · Zixuan Ni1 Hongxing Li1,2 · Yiwen Qiu1 · Weiming Lu1&Dagger; · Yongliang Shen1 1Zhejiang University   2Yuvion Team, Alibaba Group *Equal contribution   &dagger;Project leader   &Dagger;Corresponding author </div> ## 🔥 Overview **Relay-OPD** fixes *prefix failure* in on-policy distillation. A label-free **handoff trigger** — the teacher's top-1 token is a reflection token while no reflection token appears in the student's top-K — locates failed prefixes online during student generation. The teacher then briefly takes over for a short **teacher leg** of L paragraphs, hands the trajectory back, and a limited **relay budget (M, L)** keeps intervention early and local. The entire rollout runs in a single speculative decoding engine (student as draft model, teacher as target model), and the student is optimized on the relayed trajectory, including the relay tokens themselves. <div align="center"> <img src="docs/assets/teaser.png" alt="Relay-OPD teaser" width="95%"> </div> <div align="center"> <img src="docs/assets/method.png" alt="Relay-OPD method overview" width="95%"> </div> ## 📢 News - **`2026-07-29`**: 🔥🔥 We released our [paper](https://arxiv.org/abs/2607.26057) and the full training, ablation, and evaluation code. - **`2026-07`**: 🔥 Project page released. ## 📖 Results With a Qwen3-4B-Instruct-2507 teacher and Qwen3-0.6B/1.7B-Non-Thinking students on eight mathematical reasoning benchmarks, Relay-OPD achieves the best or second-best result on every benchmark — **+5.73%** over standard OPD and **+1.49%** over the strongest baseline FastOPD on average at 1.7B — while cutting average training trajectory length by **more than 50%**. ## 🛠️ Installation The implementation lives in [`relay-opd/`](relay-opd/) and is built on [verl](https://github.com/verl-project/verl). The speculative-decoding patch targets vLLM internals, so **vLLM 0.21.0** is the one mandatory version pin. ### Python environment ```bash git clone git@github.com:ZJU-REAL/Relay-OPD.git cd Relay-OPD/relay-opd conda create -n relay-opd python==3.12 -y conda activate relay-opd pip3 install -c environment/vllm-constraints.txt vllm==0.21.0 pip3 install -e . pip3 install -r requirements-relay-opd.txt ``` Verify the installation (checks CUDA execution, the math grader, and every vLLM interface patched by Relay-OPD): ```bash python environment/verify_install.py ``` For strict reproduction of our validated environment (Linux x86_64, Python 3.12, CUDA 13.0, PyTorch 2.11, vLLM 0.21.0), an optional locked installer is provided: ```bash bash environment/create_locked_env.sh ``` ### Training Each script takes paths through environment variables. The main Relay-OPD run (paper defaults: K=5, M=2, L=3; eight GPUs split as four student + four teacher): ```bash export STUDENT_MODEL=/path/to/student export TEACHER_MODEL=/path/to/teacher export TRAIN_DATA=/path/to/train.parquet export BENCH=/path/to/eval_parquets export OUTPUT_DIR=/path/to/output bash opd/scripts/relay_opd/train.sh ``` Every baseline (SFT, SeqKD, GRPO, OPD, FastOPD, TRD, SKD) and every paper ablation has a dedicated script under `opd/scripts/`. See [`relay-opd/README.md`](relay-opd/README.md) for the full script index, offline data synthesis, and the complete paper configuration. ### Evaluation ```bash RUN_NAME=relay_opd \ STEP=35 \ MODEL=/path/to/checkpoint/actor/huggingface \ DATA_DIR=/path/to/eval_parquets \ OUT_ROOT=/path/to/eval_output \ BENCHES=aime24,aime25,aime26,math500,amc23,olympiad,hmmt_feb_2026,hmmt_nov_2025 \ N_SAMPLES=32 MAX_NEW=32768 MAX_MODEL_LEN=34817 DP_SIZE=4 TP=1 \ bash opd/scripts/evaluation/math.sh ``` ## ⭐️ Citation If you find this project useful, welcome to cite us. ```bibtex @misc{xu2026passbatontrajectoryrelayedonpolicy, title={Pass the Baton: Trajectory-Relayed On-Policy Distillation}, author={Haolei Xu and Xiaowen Xu and Haiwen Hong and Zixuan Ni and Hongxing Li and Yiwen Qiu and Weiming Lu and Yongliang Shen}, year={2026}, eprint={2607.26057}, archivePrefix={arXiv}, primaryClass={cs.CL}, url={https://arxiv.org/abs/2607.26057}, } ``` ## 🤝 Acknowledgement This project builds on [verl](https://github.com/verl-project/verl) and [vLLM](https://github.com/vllm-project/vllm). We thank the authors of those projects.

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2026-07-28

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