2.于是我用deepseek r1 + rule reward正确的作为正例,采样模型本身错误的回答作为负例,去做dpo: 效果不错啊,而且居然难得的练dpo时的chosen reward没有降低,小刀剌屁股开了眼了,训练完我在测试集上测了一版,准确率也提升了很多,但是有限,由原来的50%准确率提升到70%,这也还不够啊,那咋整,ppo暂时练不起,...
所以 Iterative DPO 更类似于介于 Online 和 Offline RL 算法的中间形态。 Iterative DPO 最大的一个优点是工程实现的方便性和算法收敛效果的平衡,因为其训练为分阶段进行,也就是样本推理以及模型训练都是独立的运行阶段,通常不需要同时把所有模型加载到GPU上,也就规避了 RLHF 的 Infra 实现难题。对于推理加速也很...
This is the repository for running the Iterative DPO with rule-based rewards. In every iteration, we sample responses from the model and label the rewards using the rule-based method. We then construct the preference pair based on the reward scores for DPO training. In our code, we perform...
iterative preference optimization can lead to visually hallucinated verbose responses due to length bias within the self-rewarding cycle. To address these issues, we propose Iterative Self-Retrospective Direct Preference Optimization (ISR-DPO), a method that uses self-retrospection to enhance preference ...
OpenRLHF SFT/DPO/RewardModel/PPO trainers support--packing_samplesbased on--flash_attn Reward Model Training deepspeed --module openrlhf.cli.train_rm \ --save_path ./checkpoint/llama3-8b-rm \ --save_steps -1 \ --logging_steps 1 \ --eval_steps -1 \ --train_batch_size 256 \ --mic...
Built on assumptions regarding uncertainty and distribution shifts, we propose a comparative view to rank the implicit reward margins as predicted by DPO to select the response pairs that yield more benefits. Through extensive experiments, we show that annotating those response pairs with small margins...
optimization(Schulman et al., 2017, PPO) and direct preferenceoptimization (Rafailov et al., 2023, DPO).To continuously improve LLMs’ capability, re-cent work underlines the signif icance of iterativepreference learning, which repetitively interleavesbetween training the model and collecting online...
我们提出了Iterative Length-Regularized DPO(iLR-DPO)算法,训练得到的模型Storm-7B在Alpaca Eval 2.0(测试大模型对齐性能的主流榜单)上超过GPT-4 Preview,目前是该榜单上的最强开源模型。 图1: AlpacaEval 2.0 排行榜 如图2所示,iLR-DPO可以在不显著增加回复长度的情况下持续地将LLM与人类偏好对齐。
直接偏好优化方法:直接利用偏好数据优化模型策略,无需单独训练奖励模型,代表性工作是Direct Preference Optimization(DPO)。这种方法在稳定性和扩展性上优于基于奖励模型的方法。 尽管这些方法在优化模型与人类偏好的对齐上取得了一定进展,但它们仍然存在显著的局限性。
adndnzPdPoBdznfoBebABeBeiii2edidn22dBdnddneBnnnBCPPPnoooPe2bbbAAAoe-ibAd-d2i2n-iddndyVya)VVt-iPdtaPt-i-a-PidaVddddnnndyyyyyynVyyV,tiPaititP---Paa-nPonibPAnoPbAoibAinPobAnPnPoboAbAdnyydndyynyy-dn-yy-dndnyyyy--- Both P2VPy–block–PMMA–block–PSt (BCA) and (PMMA)–block–PSt–...