Blog
Long-form articles, tutorials, and my thoughts on software development.
MaxRL: From REINFORCE to Maximum Likelihood
Why dividing by the number of successes instead of the batch size changes what your gradient estimator optimizes — and how this connects REINFORCE, maximum likelihood, and pass@k through one clean mathematical identity.
Reinforcement Learning from Scratch
Building RL from the ground up — actions, rewards, policies, expected reward, the policy gradient theorem, and REINFORCE — all derived step by step with concrete examples.
Mathematical Prerequisites for Reinforcement Learning
Building the math foundations you need for RL — probability, expected value, derivatives, the log trick, and Monte Carlo estimation — all through one consistent example.
Manifold-Constrained Hyper-Connections: Stabilizing Deep Networks Beyond ResNets (with the actual math)
From residual identity paths to Hyper-Connections and mHC — now with the paper's exact equations, fully unrolled products, and concrete numeric examples
Manifold-Constrained Hyper-Connections: Stabilizing Deep Networks Beyond ResNets
A deep dive into why residual connections work, how Hyper-Connections generalize them, and why constraining learned skip paths to doubly stochastic matrices solves the instability problem
Gradient Boosting: A Complete Guide
A deep dive into Gradient Boosting - from intuition and geometry to the math behind pseudo-residuals, stage-wise corrections, and practical implementation considerations.
Hello, World!
A quick introduction about me and what I do.