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What are Recursive Language Models (RLMs) and How Do They Work?
A Recursive Language Model (RLM) handles very large inputs by letting the model call another language model, or itself, on smaller parts of the input.
What is Group Relative Policy Optimization (GRPO) and How Does It Work?
GRPO is a reinforcement learning algorithm to train LLMs. It generates a group of answers for the same question and teaches the model to prefer the better ones.
What is Direct Preference Optimization (DPO) and How Does It Work?
Direct Preference Optimization (DPO) trains an LLM directly on human preference data, without a separate reward model and without reinforcement learning.
What is Proximal Policy Optimization (PPO) and How Does It Work?
Proximal Policy Optimization (PPO) is a reinforcement learning algorithm that improves the policy in small, safe steps. It is used to train LLMs with RLHF.
Batch Normalization vs Layer Normalization
Batch Normalization normalizes each feature across all examples in a batch, while Layer Normalization normalizes all features within each single example.
What is RLHF? Reinforcement Learning from Human Feedback Explained
RLHF (Reinforcement Learning from Human Feedback) teaches an LLM to give the responses that humans prefer, by turning human preferences into a reward signal.
What are Autoregressive Models?
An Autoregressive Model generates one piece at a time, like one token, by predicting the next step from everything it has generated so far. GPT works this way.
What are Large Reasoning Models (LRMs) and How Are They Different from LLMs?
A Large Reasoning Model (LRM) is an LLM that thinks step by step before it answers, producing a long chain of reasoning first. This makes it better at hard problems.
What is Continuous Batching in LLMs and How Does It Work?
Continuous Batching lets LLM servers handle many more users by replacing each finished request with a new one right away, so that the GPU is never idle.