Local Optimization in Monte-Carlo Tree Search for the Traveling Salesman Problem
Published:
Enhancement of Monte-Carlo Tree Search for the Traveling Salesman Problem with 2-opt Optimization
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Download the French version here
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Aspiration-based Reinforcement Learning
Published:
Inspired by satisficing, we introduce a novel concept of non-maximizing agents, א-aspiring agents, whose goal is to achieve an expected gain of א. We derive aspiration-based algorithms from Q-learning and DQN.
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nnterp: Neural Network Interpretation Tools for Transformer Models
Published:
A Python package for analyzing and interpreting transformer model behaviors through activation analysis and interventions, based on nnsight
Separating Tongue from Thought: Activation Patching Reveals Language-Agnostic Concept Representations in Transformers
Published:
We provide causal evidence for the existence of language-agnostic concept representations within LLMs
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Using Crosscoders for Diffing Base and Instruction-Tuned Language Models
Published:
We investigate the differences between base and instruction-tuned language models using Crosscoders
Tiny-Dashboard: A Lightweight Feature Activation Analysis Tool
Published:
A minimal, hackable package for building feature activation dashboards in transformer models
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