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March 15, 2026

What You Assume vs. What You Compute

Part 4 of What Your RL Algorithm Actually Assumes — model-based vs. model-free, the assumptions table, AIXI as the incomputable ideal, and the unifying claim: representation is prior is assumption.

March 15, 2026

The Architecture Is the Prior

Part 3 of What Your RL Algorithm Actually Assumes — the architecture decides what kind of features can be learned, and that decision is a Bayesian prior over value functions.

reinforcement-learning neural-networks inductive-bias machine-learning

March 15, 2026

The Features You Choose Are the Assumptions You Make

Part 2 of What Your RL Algorithm Actually Assumes — how hand-crafted features compress the state space, and what you're betting on when you pick them.

reinforcement-learning function-approximation feature-engineering machine-learning

March 15, 2026

The Infinite Table

Part 1 of What Your RL Algorithm Actually Assumes — tabular Q-learning makes zero assumptions about state similarity and pays for it in sample complexity.

reinforcement-learning q-learning representation machine-learning

November 15, 2024

Cluster-Aware Retrieval for RAG Systems

Using GMM clustering to improve retrieval in topically diverse knowledge bases

rag machine-learning embeddings information-retrieval llm