Reinforcement Learning Course
RLHF: Aligning Language Models with Human Feedback
Part 9: From human preferences to a trained reward signal, and the four-model PPO pipeline.
Avi Chawla
401 posts published
Reinforcement Learning Course
Proximal Policy Optimization
RL Part 8: Trust regions, the clipped surrogate, and the workhorse of modern RL.
Reinforcement Learning Course
Policy Gradients: REINFORCE and Actor-Critic
RL Part 7: Learning the policy directly, from REINFORCE to actor-critic.
Reinforcement Learning Course
Introduction to Deep RL and DQN
RL Part 6: From linear features to neural networks, and the engineering choices that makes deep value-based RL possible.
Reinforcement Learning Course
Function Approximation
RL Part 5: From tables to parameterized value functions.
Memory
[Hands-on] Agent memory is only as good as its schema
A deep dive on building production-grade memory for Agents.
![[Hands-on] Agent memory is only as good as its schema](https://storage.ghost.io/c/3f/df/3fdf6ed2-17ac-4b12-a693-8078bd13e748/content/images/size/w400/2026/06/990fe7c9-7c72-49c9-8fb4-ea0b9cc8e01b.jpg)
Reinforcement Learning Course
Model-Free Learning
RL Part 4: Learning value functions and policies without a model. Monte Carlo methods, TD(0), SARSA, Q-learning, and the bias-variance bridge between them.
Agents
Hermes Agent Masterclass
Everything you need to understand and customize Hermes Agent.
LLMs
Speculative Decoding in LLMs
...explained with code and tradeoffs.
Reinforcement Learning Course
Bellman Equations and Dynamic Programming
RL Part 3: Bellman expectation and optimality equations, policy iteration, value iteration, and why dynamic programming needs a model.
Reinforcement Learning Course
Markov Decision Processes and Value Functions
RL Part 2: Markov decision processes, returns, policies, and value functions.
Reinforcement Learning
How to Beat GRPO Without Touching Model Weights
Berkeley beat GRPO by 10 points with 35× fewer rollouts and no GPU training,
Reinforcement Learning
How Top AI Labs Are Building RL Agents in 2026
The era of not writing custom reward functions.
Reinforcement Learning Course
Foundations of Reinforcement Learning
RL Part 1: Agents, environments, rewards, and why RL is different from supervised learning.
Claude
How We Cut Our Claude Code Token Usage 2.8x!
...using Karpathy's context engineering principles!
Classical ML and Deep Learning
Diffusion LLMs from the Ground Up: Training, Inference, and Practical Engineering
Diffusion LLMs Part 2: How dLLMs scale to 100B parameters, the inference stack that makes them fast, hands-on code, and when to actually use them.
LLMOps
72 Techniques to Optimize LLMs in Production
...explained with usage.
Claude
10 Must-use Slash Commands in Claude Code
...explained with exact prompts and usage!
Agents
Build Agents That Never Forget
A first-principles walk through agent memory (open-source).
Classical ML and Deep Learning
Diffusion LLMs from the Ground Up: Theory, Math, and Why They Work
Diffusion LLMs Part 1: Understanding how diffusion language models work from first principles, the math behind masked diffusion, and why they represent a fundamentally different approach to text generation.
Agents
Advisor Strategy in Agents
Reduce token costs and improve performance...and how to use it with Claude!
Claude
The Anatomy of an Agent Harness
A deep dive into what Anthropic, OpenAI, Perplexity and LangChain are actually building.
MLOps/LLMOps Course
MLOps and LLMOps: Case Studies
An exploration of real-world MLOps and LLMOps case studies, examining the importance of reliable ML and AI engineering and their significance for business outcomes.
Claude
Anatomy of the .claude/ Folder
A complete guide to CLAUDE.md, custom commands, skills, agents, and permissions, and how to set them up properly.
MLOps/LLMOps Course
Concepts of LLM Serving
LLMOps Part 14: An overview of the fundamentals of LLM serving, including API-based access, inference with vLLM, and practical decisions.
MLOps/LLMOps Course
LLM Inference and Optimization: Fundamentals, Bottlenecks, and Techniques
LLMOps Part 13: Exploring the mechanics of LLM inference, from prefill and decode phases to KV caching, batching, and optimization techniques that improve latency and throughput.
Claude
Claude Subagents vs. Agent Teams
...explained visually.
MLOps/LLMOps Course
LLM Fine-tuning: Techniques for Adapting Language Models
LLMOps Part 12: Understanding LLM fine-tuning, parameter-efficient methods like LoRA and QLoRA, and alignment techniques such as RLHF, DPO, and GRPO.
LLMs
Paged Attention in LLMs
...explained visually!
