RLHF

Fri, 15 May 2026 00:00:00 +0000

RLHF (Reinforcement Learning From Human Feedback) involves human evaluations or rankings of model outputs as reward signals; this means that humans are involved in the process to guide the model toward human-preferred behaviors.

This is in contrast with RL in the context of reasoning, where the models rely on automated or environment-based reward signals (more objective but potentially less aligned with human preference).

RL

Wed, 13 May 2026 00:00:00 +0000

RL (Reinforcement Learning) aims to improve an LLM’s reasoning capabilities by encouraging it to take actions that lead to high reward signals.

While inference-time compute scaling improves model’s reasoning performance without modifying the model, RL updates the model’s weights during training, enabling the model to learn through trial and error based on the feedback from the environment.

It is important to distinguish reinforcement learning in the context of reasoning from RLHF, which is used during preference tuning. Both settings use reinforcement learning principles but they differ primarily in how the reward is obtained and validated (through human verifiers for RLHF versus automated verifiers or environments for reasoning RL).

Reinforcementlearning on davethehuman.com

RLHF

RL