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
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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
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…
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…
PROWL-1 is a training framework from the AI lab Odyssey in which a reinforcement learning agent is paid to break a world model.
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.
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…
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.
Reinforcement Learning with Verifiable Rewards (RLVR) is a post-training paradigm for large language models in which the reward signal comes from a deterministic
SkyRL is an open-source reinforcement learning (RL) library for post-training large language models and multi-turn agents.
Together Post-Training is a managed service from Together AI for customizing open-weight large language models with reinforcement learning (RL) and supervised fine-tuning (SFT).