Training & Optimization

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Browse subtopics: Reinforcement Learning

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GRPO

Group Relative Policy Optimization (GRPO) is a reinforcement learning algorithm for fine-tuning large language models that eliminates the separate critic (value) network used by PPO

AI Inference Chinese AI

HuggingFace TRL

TRL (Transformer Reinforcement Learning, now stylized as Transformers Reinforcement Learning) is an open-source Python library maintained by Hugging Face for post-training large language models with…

Open Source AI Reinforcement Learning

KTO (Kahneman-Tversky Optimization) is a method for aligning large language models with human feedback using only a binary signal of whether a model output is desirable or undesirable, rather than the paired…

AI Alignment AI Inference

PROWL-1

PROWL-1 is a training framework from the AI lab Odyssey in which a reinforcement learning agent is paid to break a world model.

Machine Learning Reinforcement Learning

Policy gradient methods

Policy gradient methods are a family of reinforcement learning algorithms that directly parameterise the agent's policy and optimise it by stochastic gradient ascent on the expected return.

Machine Learning Reinforcement Learning

Proximal Policy Optimization (PPO)

Proximal Policy Optimization (PPO) is an on-policy policy gradient reinforcement learning algorithm that stabilizes training by clipping the policy update so the new policy stays close ("proximal") to the old…

Machine Learning Reinforcement Learning

RLOO (REINFORCE Leave-One-Out)

RLOO (REINFORCE Leave-One-Out) is an online reinforcement learning algorithm for aligning large language models with reward signals such as those derived from human preferences.

AI Alignment Reinforcement Learning

RLVR

Reinforcement Learning with Verifiable Rewards (RLVR) is a post-training paradigm for large language models in which the reward signal comes from a deterministic

AI Inference Reasoning Models