Part 1 of a Series
#Exploration: A Study of Count-Based Exploration for Deep...
Count-based exploration algorithms are known to perform near-optimally when used in conjunction with tabular reinforcement learning (RL) methods for solving small discrete Markov decision...
Letta
Making machines that learn. Create stateful agents that remember everything, learn continuously, and improve themselves over time.
Building a Semi Autonomous Bluesky Agent with Persistent Memory - Brady Hawkins
Part 1 of building an Agentic AI with augmented memory
Import AI 455: AI systems are about to start building themselves.
The first step towards recursive self improvement
On working machines
In part one, on thinking machines, I explored two facets of the philosophy of artificial intelligence: “intelligence”, and consciousness. That left an important topic to consider for this post: the...
Dario Amodei — Machines of Loving Grace
How AI Could Transform the World for the Better
Dario Amodei — Machines of Loving Grace
How AI Could Transform the World for the Better
Solving a Million-Step LLM Task with Zero Errors
LLMs have achieved remarkable breakthroughs in reasoning, insights, and tool use, but chaining these abilities into extended processes at the scale of those routinely executed by humans,...
AI as a Tool for the Mental Load | Brittany Ellich | Offprint
AI didn't make me faster at tasks. It took over the tracking, the invisible remembering that runs a household, and gave me back creative energy I forgot I had
ProPublica’s Approach to AI
The principles and guardrails underlying our practices with artificial intelligence.
Training AI Agents with RL | Unsloth Documentation
Learn how to train AI agents for real-world tasks using Reinforcement Learning (RL).
Paper page - Harness-1: Reinforcement Learning for Search Agents with State-Externalizing Harnesses
Join the discussion on this paper page
