*label smoothing 有效果,因为避免training loss下降太快,防止过拟合,更确保泛化性; *adaptive magin 和 label smoothing同时使用效果最好; 在所有数据的实验,说明adaptive margin能够针对所有preference data提高性能。 3.4 How to Better Model Human Preference? 在original validation set,GPT-4 labeled dataset 和 s...
作者省却了reward model training,在训练过程中通过设计包含正负样本对比的loss,训练得到满足人类偏好的finetune model. 此方法在文本总结+单轮对话上提升了生成质量。 前言 首先看一眼RLHF 和 direct preference optimization (DPO) 的对比 RLHF(左),DPO(右) 这个第一眼看上去,就感觉左右两边最大的区别就是有没...
loss_value= torch.sum(sub_first_rewards.exp(), -1).log().mean()returnloss_value 3. Training language models to follow instructions with human feedback reward model: 6B 训练loss: 其中K为候选句子的个数(K等于4到9之间的值),y_w是标记者选出来更好的那个句子。这个loss需要最小化。 这个loss结...
然后在上面further pretrain”的核心区别是RLHF让生成模型可以实时和reward model交互来进行学习，从而提高...
loss 也并不是以找到这个边界为指标，而是优化正例和负例之间的差别。这使得当前的 reward model 没有...
Hi! I have got an infinite loss when trained critic model at step 2: Epoch 1/1 with loss inf I've found a source of this problem: reward model loss is calculated with unstable formula: DeepSpeedExamples/applications/DeepSpeed-Chat/training/utils/model/reward_model.py ...
gap:在广泛采用的 PbRL pipeline 中,PVRL 挑战也出现在从 pretrain 到 online training 的过渡过程中,但在以前的研究中被忽视了。在 noisy feedback 下,忘记预训练策略的问题变得更加重要,详见第 4.2 节。 (这里的预训练指的是 pebble 等工作的 比如说 最大熵预训练策略。 引出reward model 的热启动。3...
训练时因为有grountruth trajectory,所以我们可以直接并行输入trajectory tokens,然后输出所有的预测节点,每个节点是一个V维的向量,经过softmax,得到概率分布,再和groundtruth做交叉熵,得到log似然值(最小化交叉熵,相当于在最大化log似然),于是training loss可以写成...
induced by the training process.If a modelisgoal-directed with respect to some goal, it is because such goal-directed cognition was selected for.Furthermore, it should be obvious that any learned goal will not be "get more reward", but something else. The model doesn't even see the ...
Reinforcement learning with human feedback for aligning large language models (LLMs) trains a reward model typically using ranking loss with comparison pairs. However, the training procedure suffers from an inherent problem: the uncontrolled scaling of reward scores during reinforcement learning due to ...