# Nov 30, 2025

## RL is more information inefficient than you thought

- **Reinforcement Learning (RL) is significantly less information-efficient than supervised learning**, requiring far more compute to achieve a single sample due to its reliance on lengthy trajectories for reward signals, which results in lower information density per sample.

## Matrix Core Programming on AMD CDNA Architecture

- **Matrix Cores in AMD's CDNA™3 and CDNA™4 architectures** significantly enhance performance for matrix operations, achieving up to **64x speedup** with low-precision data types like FP4 compared to FP32, particularly in AI and HPC workloads.

## Program-of-Thought Prompting Outperforms Chain-of-Thought by 15%

- **Program of Thoughts (PoT)** enhances language models by **separating reasoning from computation**, using Codex to articulate reasoning as a program and an external computer for calculations.

## Dynamic Skillset Reference Architecture

- The **Dynamic Skillset Reference Architecture** enables AI agents to **introspect and dynamically load skillsets** based on user intent, enhancing adaptability and performance without static configurations.

## A new framework for causal transformer models on non-language data: sequifier

- **Sequifier** is a newly released framework for training **causal, autoregressive transformer models** on non-language data, validated through extensive use in real-world applications, including modeling **sperm whale language** and **neural activity in mice**.

## What AI may learn from the brain in adapting to continuously changing environments

- **Biological brains** exhibit rapid adaptability to new tasks, often achieving significant performance shifts within a few trials, a capability that current AI systems lack.

## Outcome-based learning vs vector search: 100% vs 3.3% accuracy on adversarial queries (p=0.001) - looking for feedback on approach

- **Outcome-based learning achieved a remarkable 100% accuracy on adversarial queries**, significantly outperforming vector search's 3.3% accuracy, highlighting the potential of integrating outcome effectiveness into retrieval systems.