RLHF 是把人类偏好作为奖励信号来微调大语言模型的方法，通常先做监督微调、再训练奖励模型拟合人类偏好、最后用强化学习算法优化策略，核心是让模型的输出从能生成内容变成符合人类期望与价值观的表达，能力包括偏好数据采集、奖励模型训练与强化学习策略优化。它解决的是预训练模型输出虽然流畅但无用、无害性不足的问题，并不解决偏好数据本身存在偏差导致的对齐偏差问题。
RLHF 的核心是把人的偏好变成训练信号，它可以帮助企业实现以下目标：
让模型学会按指令回答而不是自说自话。监督微调阶段建立的指令跟随能力成为后续优化的起点。监督微调阶段建立的指令跟随能力成为后续优化的起点，没有这一步，强化学习阶段会缺少稳定的行为基线。
让「哪个回答更好」变成可学习的量。人类对多个输出的排序被转成奖励模型的打分，偏好因此可被优化。奖励的不同权重组合能产出偏重能力或偏重安全的模型，团队可以按场景选择折中点而非只有一种默认配置。
让有用与无害可以分别调节。奖励的不同权重组合能产出偏重能力或偏重安全的模型，而非只有一种折中。
让对齐结果可被替代方案继承。直接偏好优化等无需强化学习的方法沿用了同一套偏好数据，路径更省算力。
总之，RLHF 可以帮助企业实现大模型从能说话到会说话且说人话的转变，把人类价值判断注入模型行为而非仅靠提示约束，是理解大模型后训练与对话能力来源的关键环节。
翻译
RLHF is a method for fine-tuning large language models using human preference as the reward signal, typically proceeding through supervised fine-tuning, then training a reward model to fit human preferences, and finally optimizing the policy with a reinforcement learning algorithm. Its core is turning model output from merely generable into an expression aligned with human expectations and values, with capabilities covering preference data collection, reward model training and policy optimization. It solves the problems of pretrained models producing fluent but useless output and of insufficient harmlessness, but it does not solve the problem of alignment bias caused by biased preference data.
At the heart of RLHF is turning human preference into a training signal, which can help companies achieve the following:
Teach the model to answer instructions rather than talk at large. Instruction-following established during supervised fine-tuning becomes the starting point for later optimization.
Make better answers a learnable quantity. Human rankings among multiple outputs are converted into reward model scores, so preferences become optimizable.
Allow usefulness and harmlessness to be adjusted separately. Different reward weight combinations yield models weighted toward capability or toward safety rather than a single compromise.
Let alignment results carry over to alternatives. Methods like direct preference optimization reuse the same preference data without reinforcement learning, saving compute.
In short, RLHF can help enterprises shift large models from speaking to speaking in ways that serve people, inject human value judgments into model behavior rather than relying only on prompt constraints, and constitute the key to understanding post-training and the origin of conversational ability.
