Before today’s topic, a quick note—I stepped away from the newsletter in April after a family loss since I needed time to slow down and just be. I’m back and excited to restart One Minute NLP: one slide, one concept, once a week to help you keep up with NLP/GenAI.

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Model Context Protocol

Model Context Protocol (MCP) is an open standard for giving language models structured access to tools, data, and prewritten prompts. Instead of relying on ad-hoc prompt engineering or custom retrieval pipelines, tools and data sources are exposed to models through MCP servers in a standardized format. Developers can plug in existing tools (both cloud e.g., GitHub, Slack and local e.g., company database) rather than reinvent integrations each time. This approach promotes reusability across models and projects. An MCP client (usually inside an AI app or agent—an MCP Host) is used to query these servers to retrieve information or invoke actions.

For MCP to work, the model (or its agent wrapper) must support tool use. MCP doesn’t overcome model limits like context window size or reasoning errors; it merely provides a standard for managing model context.

Further reading

• Build an MCP Server and Build an MCP Client tutorials by ModelContextProtocol.io — The official MCP documentation is a great place to get started with MCP. The tutorials are easy to follow and can give you a solid grasp of how MCP Servers and Clients work.

• Model Context Protocol servers — This is a very comprehensive list of existing MCP servers.

• Everything Wrong with MCP by Shrivu Shankar — This article does a great job explaining the limitations of MCP.

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In-context learning

In-context learning is a prompting technique where LLMs learn how to handle new tasks using examples in the prompt, without any weight updates. Unlike training or fine-tuning, which changes model parameters, in-context learning utilizes input-output pairs provided in the prompt to guide LLM behavior, even for tasks the LLM wasn’t explicitly trained on.

This allows rapid task adaptation without the compute or data needed for fine-tuning. Supplying examples is called few-shot learning; using none is zero-shot. Few-shot prompting often outperforms zero-shot on many tasks.

Performance depends heavily on the task, the number and order of examples, and how well they’re chosen. A few well-curated examples can match the performance of a fully fine-tuned model, while poorly selected ones can significantly degrade it.

Further reading

• Language Models are Few-Shot Learners by Brown et al. — This paper introduced GPT-3 and the concept of in-context learning.

• Prompt Engineering by Lilian Weng — This fantastic article explains many prompt engineering techniques including few-shot prompting and provides tips for selecting good examples.

• Practical Prompt Engineering by Cameron Wolfe — Drawing on lots of research articles, this deep dive covers many common prompting techniques including few-shot prompting.

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Reflection agents

Reflection is a prompting strategy used in agent workflows to iteratively improve an LLM’s reasoning or outputs. Instead of directly returning a final answer, the LLM is first prompted to reflect on its initial response and identify errors, gaps, or opportunities for improvement. It then uses this feedback to generate a revised response. This can be repeated multiple times until some stopping condition is met (e.g., max iterations, token limit, or some evaluation criteria).

This process has been shown to improve performance over direct response generation on a variety of tasks. Some agents add an explicit evaluation step which is called prior to reflection. Evaluation can be done via an LLM judge, external tools (e.g., a code compiler or a knowledge base) or other methods (e.g., heuristic score). Reflection can be used in combination with other agent design patterns such as the ReAct framework.

Further reading

• Reflexion: Language Agents with Verbal Reinforcement Learning by Shinn et al. — A seminal paper on reflection which used separate evaluation and reflection modules. This paper is a great read if you want to understand reflection more in depth. The appendix includes the prompts the authors used in their experiments.

• If you want to try running a reflection agent yourself, both LangChain and LlamaIndex include different variations of reflection agents.

• Agents by Chip Huyen — this fantastic blog post covers much more than just Reflection, but if you are looking to understand agents and where Reflection fits in, this is a great place to start.

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Top-k and top-p sampling

Sampling is a method LLMs use to generate output tokens, with the next token selected randomly based on learned output token probabilities. Higher-probability tokens are more likely to be chosen, but random sampling can generate odd or nonsensical outputs if low-probability tokens are picked.

Top-k sampling truncates the distribution to the k most probable tokens, then randomly picks from this subset using renormalized probabilities. Top-1 sampling is equivalent to greedy decoding which always picks the most probable token. Top-k sampling can struggle if the top k tokens include low-probability tokens or omit likely tokens. Top-p sampling (nucleus sampling) addresses this by keeping the top p percent of the probability mass instead.

Top-k and top-p sampling can be used with or as alternatives to temperature sampling.

Further reading

• Speech and Language Processing by Jurafsky and Martin (free to read online) — Section 10.8 (Large Language Models: Generation by Sampling) provides a great introduction to sampling from language models.

• How to generate text: using different decoding methods for language generation with Transformers — If you prefer to learn with code, this Hugging Face blog post includes code examples alongside explanations of the most popular sampling techniques.

• The Curious Case of Neural Text Degeneration by Holtzman et al. — This paper which introduced top-p sampling includes comparisons of different sampling techniques and their pros and cons.

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Reasoning Models

Reasoning models are a new class of LLMs designed to solve complex problems like math and coding. Unlike standard LLMs that generate an answer directly, reasoning models are designed and trained to first produce intermediate “thinking” steps (similar to chain-of-thought reasoning, but longer and more detailed) before finalizing a response. This makes them strong at multi-step logic tasks (e.g., math proofs, coding challenges), but less efficient for simpler tasks like translation. Reasoning models are trained using reinforcement learning (RL). For example, DeepSeek-R1 was trained using a combination of RL from human preference (RLHF) and RL using verifiable rewards (e.g., math correctness).

Reasoning models are typically slower and more expensive compared to standard LLMs because they require more output tokens for thinking steps.

Further reading

• Demystifying Reasoning Models by Cameron Wolfe — Easy to follow article on reasoning models that explains what they are and how they are trained. Includes lots of helpful references.

• LLM Post-Training: A Deep Dive into Reasoning Large Language Models by Kumar et al. — If you want to dive deeper, this paper gives a thorough overview post-training techniques, including the RL techniques that underpin models like DeepSeek-R1.

• Reasoning best practices (OpenAI Platform documentation) — This page gives examples of problems where OpenAI’s reasoning models have been found to work well and includes tips for prompting reasoning models effectively.

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Group Relative Policy Optimization

Group Relative Policy Optimization (GRPO) is a new reinforcement learning algorithm that can be used in Reinforcement Learning (RL) for LLMs in place of Proximal Policy Optimization (PPO). In GRPO, multiple responses are generated from the same prompt using the LLM (also called policy) being trained. GRPO uses their average reward (their ‘quality’) as a baseline for computing each answer’s advantage relative to the average reward of the group; the advantage is used to calculate gradients for the policy LLM. This eliminates the need for a separate critic model (like in PPO) to calculate advantage, thereby using much less memory and compute. Like PPO, GRPO constrains the size of weight updates to prevent drastic behavior changes.

GRPO was used to train DeepSeek R1 in math and coding-based RL (using rule-based rewards) and RL from Human Feedback (RLHF). GRPO, like PPO, still depends heavily on the quality of the reward signal.

Further reading

• DeepSeekMath: Pushing the Limits of Mathematical

  Reasoning in Open Language Models by Shao et al. — This paper introduced GRPO. The power of the algorithm was later demonstrated in DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning by Guo et al. which introduced the DeepSeek-R1 model.

• A vision researcher’s guide to some RL stuff: PPO & GRPO by Yuge Shi — This blog post explains PPO and GRPO, and covers differences between the algorithms and how they are used in RLHF.

• The Transformer Reinforcement Learning (TRL) library by Hugging Face includes a GRPO Trainer. The documentation includes an example showing how to use GRPO to train a model.

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Proximal Policy Optimization

Proximal Policy Optimization (PPO) is a reinforcement learning (RL) algorithm that is frequently used in Reinforcement Learning from Human Feedback (RLHF) to further train LLMs after supervised fine-tuning. In RLHF, an LLM output receives a numerical reward that represents human preference; the goal is to maximize this reward. PPO uses an actor-critic setup, training two models simultaneously. The actor (also called policy) is the LLM that generates tokens; the critic estimates the expected final reward at each token—these estimates are used to calculate gradients for the policy LLM. PPO constrains the size of weight updates on the policy LLM at each training step to prevent drastic and unpredictable behavior changes.

PPO is the standard algorithm for RLHF; it was used to fine-tune InstructGPT and ChatGPT. PPO doesn’t prevent reward hacking—achieving high rewards through learning to exploit imperfections in the reward model.

Further reading

• These series of three posts on RL for LLMs by Cameron Wolfe do a fantastic job of explaining both the intuition and the math behind RL algorithms in an accessible way: Basics of Reinforcement Learning for LLMs, Policy Gradients: The Foundation of RLHF, Proximal Policy Optimization (PPO): The Key to LLM Alignment.

• The N Implementation Details of RLHF with PPO by Costa Huang et al. — This ICLR blog post walks through a Python implementation of RLHF with PPO.

• Detoxifying a Language Model using PPO by Hugging Face shows how to use the TRL (Transformer Reinforcement Learning) library to apply PPO to reduce toxicity of an LLM.

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RLHF

Reinforcement Learning from Human Feedback (RLHF) is a training phase used on LLMs after supervised fine-tuning to further improve LLM responses. In contrast to supervised fine-tuning, where a model learns to mimic responses for given prompts, in RLHF a model learns by generating a response and then receiving a score indicating how good that response is. Because using humans to assign response scores is expensive, scores are typically generated using an LLM reward model (RM) trained on human preference pairs (prompt, winning response, losing response). RLHF uses a reinforcement learning algorithm like proximal policy optimization (PPO) to update model parameters based on the scores from the RM.

RLHF was one of key innovations behind ChatGPT. Collecting high-quality data for training a RM can be very resource intensive. RLHF is often used to improve helpfulness and minimize harmfulness of model responses.

Further reading

• Learning to summarize with human feedback by Stiennon et al. and Training language models to follow instructions with human feedback by Ouyang et al. were the first works that applied RLHF to LLMs.

• RLHF: Reinforcement Learning from Human Feedback by Chip Huyen — This blog post provides an easy to follow explanation of RLHF and the intuition behind it. If you want to go a bit deeper, check out The Story of RLHF: Origins, Motivations, Techniques, and Modern Applications by Cameron Wolfe.

• If you prefer videos, have a look at Reinforcement Learning with Human Feedback (RLHF) in 4 minutes by Sebastian Raschka.

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ReAct Agent Model

ReAct is an agent design pattern that incorporates reasoning (thinking through steps logically) and action (interacting with the environment). Prior prompting strategies either generate reasoning (e.g., chain-of-thought) or take actions (e.g., tool use) separately. In ReAct, an LLM is prompted to plan its next action(s), take action(s), and reflect on results – this is repeated until the agent considers a task complete. ReAct has been shown to perform well on complex tasks like multi-hop question answering and has become a common agent pattern built upon by other approaches like Reflexion.

ReAct may struggle if outcomes of actions it takes are incomplete or incorrect. It can have higher cost/latency than simpler methods due to more steps/tokens required. It has been shown to perform best with fine-tuning.

Further reading

• ReAct: Synergizing Reasoning and Acting in Language Models by Yao et al. — This paper introduced the ReAct Agent Model. This blog post by the paper’s authors provides a concise summary of the paper.

• Both Hugging Face Transformers and LangChain LangGraph provide implementations of ReAct. You can view the ReAct prompt used by LangChain in the LangChain Hub.

• Agents by Chip Huyen — this fantastic blog post covers much more than just ReAct, but if you are looking to understand agents and where ReAct fits in, this is a great place to start.

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Knowledge Distillation

Knowledge Distillation (KD) is a form of model compression used to transfer knowledge from a large, powerful teacher model to a (typically) smaller, more efficient student model. In contrast to supervised learning where a model is trained using labeled data (inputs and expected outputs — also known as hard targets), in KD, the student is typically also trained using the teacher’s reasoning (soft targets). Different methods exist for extracting the teacher’s reasoning, such as using its weights, analyzing the probabilities it assigns to possible outputs, or generating a rationale.

A popular Transformer model DistilBERT is a distilled version of BERT. It’s 40% smaller and 60% faster, while retaining over 95% of BERT’s performance. Some speculate that GPT-4o is a distilled version of some larger model.

Further reading

• Distilling the Knowledge in a Neural Network by Hinton et al. — This seminal paper formulated the concept of knowledge distillation.

• A Survey on Knowledge Distillation of Large Language Models by Xu et al. — If you want to dive deeper, this recent survey provides an overview of different methods for LLM distillation.

• The SetFit library by HuggingFace for fine-tuning sentence transformers provides a Knowledge Distillation guide. OpenAI also provides an API for knowledge distillation.

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Mixture of Experts

Mixture of Experts (MoE) is an ensemble learning technique that trains multiple “expert” (sub-)models and a “gate” network that determines which expert(s) to use for a particular input. In Language Models, MoE is typically implemented as a sparse layer composed of multiple expert sub-layers and a router; the experts can be a simple feed forward network (FFN) or a more complex network, and the router is typically a linear layer with a softmax function. MoE enables larger models (more parameters) while still being cheap during training and inference because only a subset of the experts is active for a given input (this is called conditional computation).

Mixtral 8x7B is an example of a MoE. In Mixtral, the feed forward layer of each transformer block is replaced by a MoE layer. Each token in a given input sequence activates a different set of experts.

Further reading

• Mixture of Experts Explained by Sanseviero et al. — This article provides an overview of MoE including the history, challenges, and current developments.

• A Visual Guide to Mixture of Experts (MoE) by Maarten Grootendorst — If you are a visual learner, this guide does a great job of breaking the technique down into individual components and explaining the intuition behind them.

• A Survey on Mixture of Experts by Cai et al. — If you want to dive deeper, this survey covers the different ways of building MoE models.

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Low-Rank Adaptation

Low-Rank Adaptation (LoRA) is a popular method for Parameter-Efficient Fine-Tuning (PEFT) of Large Language Models. Fine-tuning a LLM can significantly improve performance but can be prohibitively costly due to model size. Instead of updating all weights, LoRA freezes the original weights W and only trains a weight update ΔW represented as two smaller rank decomposition matrices A and B which are much more efficient to train and store. During inference, this weight update is added to the original weights.

For example, LoRA can reduce the number of trainable parameters of GPT-3 from 175B to 37.7M while performing as well as if all weights were fully fine-tuned. LoRA can be applied to any subset of weight matrices of a model (most commonly the attention and/or the feedforward layers). Multiple LoRA modules can be trained for different tasks and swapped during inference.

Further Reading

• LoRA: Low-Rank Adaptation of Large Language Models by Hu et al. — This paper first introduced the LoRA technique.

• Scaling Down to Scale Up: A Guide to Parameter-Efficient Fine-Tuning by Lialin et al. — this paper presents a detailed survey (albeit a bit outdated) and a taxonomy of parameter-efficient fine-tuning methods.

• PEFT is an implementation of various Parameter-Efficient Fine-Tuning techniques including LoRA built by Hugging Face.

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Sampling is a common method used by LLMs for generating output tokens: the next token is randomly chosen based on the token probabilities learned by the LLM. Temperature sampling affects the shape of the probability distribution of tokens by introducing a scaling factor τ (temperature):

i.e., the raw model scores are divided by τ before normalizing with softmax.

If τ=1.0, the probabilities are unchanged. When τ is closer to 0, the probability of high-probability words is increased and probability of low-probability words is decreased (i.e., randomness is reduced and the model is more likely to pick a high-probability word). Setting τ to a value greater than 1 has the opposite effect. Top-k and top-p sampling are alternatives to and can be used in conjunction with temperature sampling.

Further Reading

• Your settings are (probably) hurting your model - Why sampler settings matter (reddit.com/r/LocalLLaMA post by kindacognizant) — this post provides a fantastic explanation of how different temperature values affect model outputs and how temperature works together with top-k and top-p sampling.

• The Effect of Sampling Temperature on Problem Solving in Large Language Models by Renze and Guven — This recent work presents a detailed investigation of the effect of different temperature settings on LLM performance on MCQA problems.

• Speech and Language Processing by Jurafsky and Martin (free to read online) — Section 10.8 (Large Language Models: Generation by Sampling) provides a great explanation of different sampling techniques.

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Byte-Pair Encoding Algorithm

Byte-Pair Encoding (BPE) is a tokenization algorithm which is used by most LLMs (e.g., GPT, Llama) to build their vocabularies. Tokens created by BPE are called subwords because they are often smaller than words – these are typically the most common subwords that appear in the pretraining set. To determine these subwords, BPE starts with individual characters as the token set and then iteratively merges the two most common consecutive tokens to form a new token until a target vocabulary size is reached. BPE can deal with unknown words during inference because new words can be represented by some sequence of existing subwords. WordPiece and SentencePiece tokenization are popular alternatives to BPE and work similarly.

Consider an example corpus: “llama llama red pajama“.

In BPE tokenization, the starting vocabulary consists of individual characters. The corresponding corpus representation would look like this (dashes indicate token boundaries):

Iteration: 0

Vocabulary: l a m r e d p j

Corpus representation: l-l-a-m-a l-l-a-m-a r-e-d p-a-j-a-m-a

Vocabulary and corpus representation after the first iteration (a+m were the most frequent consecutive tokens and are merged into a new token am):

Iteration: 1

Vocabulary: l a m r e d p j am

Corpus representation: l-l-am-a l-l-am-a r-e-d p-a-j-am-a

Vocabulary and corpus representation after the second iteration:

Iteration: 2

Vocabulary: l a m r e d p j am ama

Corpus representation: l-l-ama l-l-ama r-e-d p-a-j-ama

Vocabulary and corpus representation after the fifth iteration:

Iteration: 5

Vocabulary: l a m r e d p j am ama ll llama re

Corpus representation: llama llama re-d p-a-j-ama

Further Reading

• Neural Machine Translation of Rare Words with Subword Units by Sennrich et al. — This work was the first to introduce the BPE algorithm to NLP through an application in Neural Machine Translation.

• Summary of the Tokenizers (Hugging Face docs) — this page provides a fantastic overview of different tokenization techniques including a detailed explanation of the BPE algorithm.

• There are many implementations of BPE, three notable ones include the tiktoken library (used by OpenAI’s models), minbpe (Andrej Karpathy’s minimal implentation), and sentencepiece (Google’s implementation of BPE which, unlike the original implementation, doesn’t require input text to be split into words).

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Perplexity

Perplexity (usually abbreviated PP or PPL) is a metric commonly used to evaluate language models. Perplexity measures how uncertain (or “perplexed”) a model is about the predictions it makes. The lower the perplexity, the better a model predicts the test set. Perplexity usually correlates well with improvements on real world tasks, but it is not a guarantee of better task performance. The perplexity of two models is only comparable if they use the same vocabularies.

Given an example text sequence X: The quick fox jumps over the lazy dog, we can calculate PP using the probability (log-likelihood) of predicting the next word given the words that came before it:

where

and N is the number of words in the sequence (N=8 for our test sequence).

Formally:

Further Reading

• Speech and Language Processing by Jurafsky and Martin (free to read online) — Section 3.3 (Evaluating Language Models: Perplexity) provides a great explanation of the metric.

• Lessons from the Trenches on Reproducible Evaluation of Language Models by Biderman et al. — this paper includes some practical considerations for using perplexity to compare language modeling performance of different models (Appendix A.3).

• Perplexity of fixed-length models (Hugging Face docs) — how to calculate perplexity with the Hugging Face Transformers library.

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