Reinforcement learning from human feedback (RLHF) aligns large language
models (LLMs) by encouraging their generations to have high rewards, using a
reward model trained on human preferences. To prevent the forgetting of
pre-trained knowledge, RLHF usually incorporates a KL regularization; this
forces the policy to remain close to its supervised fine-tuned initialization,
though it hinders the reward optimization. To tackle the trade-off between KL
and reward, in this paper we introduce a novel alignment strategy named Weight
Averaged Rewarded Policies (WARP). WARP merges policies in the weight space at
three distinct stages. First, it uses the exponential moving average of the
policy as a dynamic anchor in the KL regularization. Second, it applies
spherical interpolation to merge independently fine-tuned policies into a new
enhanced one. Third, it linearly interpolates between this merged model and the
initialization, to recover features from pre-training. This procedure is then
applied iteratively, with each iteration's final model used as an advanced
initialization for the next, progressively refining the KL-reward Pareto front,
achieving superior rewards at fixed KL. Experiments with GEMMA policies
validate that WARP improves their quality and alignment, outperforming other
open-source LLMs.

使用人类反馈进行强化学习的 WARP 方法，在保持预训练知识的同时优化奖励函数，经过迭代逐渐改进 KL - 奖励的权衡，提升了 GEMMA 策略的质量和对齐度。

WARP: 关于加权平均奖励策略的好处

WARP: On the Benefits of Weight Averaged Rewarded Policies

Socio-demographic prompting is a commonly employed approach to study cultural
biases in LLMs as well as for aligning models to certain cultures. In this
paper, we systematically probe four LLMs (Llama 3, Mistral v0.2, GPT-3.5 Turbo
and GPT-4) with prompts that are conditioned on culturally sensitive and
non-sensitive cues, on datasets that are supposed to be culturally sensitive
(EtiCor and CALI) or neutral (MMLU and ETHICS). We observe that all models
except GPT-4 show significant variations in their responses on both kinds of
datasets for both kinds of prompts, casting doubt on the robustness of the
culturally-conditioned prompting as a method for eliciting cultural bias in
models or as an alignment strategy. The work also calls rethinking the control
experiment design to tease apart the cultural conditioning of responses from
"placebo effect", i.e., random perturbations of model responses due to
arbitrary tokens in the prompt.

系统研究了四种 LLMs 随不同类型提示文本在不同类型数据集上的回应变化，对于文化条件提示作为检验模型文化偏见或对齐策略的方法的鲁棒性提出了疑问，同时呼吁重新思考控制实验设计以区分回应的文化条件与 ' 安慰剂效应 ' 之间的关系。