#Reinforcement Learning

10 posts tagged with "Reinforcement Learning"

Does RL Actually Make LLMs Smarter? A Critical Look at Reinforcement Learning for Reasoning

2025-12-30T13:00:00Z•13 min read

#reinforcement learning #llm reasoning #rlvr #machine learning #ai research

Recent research suggests RL training optimizes search efficiency over existing capabilities rather than expanding reasoning capacity. Here's what the pass@k evidence actually shows.

Project Silicon: What If We Could Do Gradient Descent on Assembly Code?

2025-12-30T11:00:00Z•16 min read

#machine learning #systems programming #program synthesis #reinforcement learning #differentiable computing

A deep dive into Project Silicon's proposal to build differentiable CPU simulators, enabling gradient-based optimization of assembly code and opening a new frontier in neural algorithm synthesis.

Benchmarks vs RL Environments: Why the Distinction Actually Matters

2025-12-18T12:00:00Z•16 min read

#reinforcement learning #benchmarks #machine learning #research methodology

Understanding when you're working with an environment versus a benchmark changes how you design experiments, interpret results, and communicate findings. This guide covers the practical differences every RL practitioner should know.

Why 1000-Layer Networks Finally Work for Reinforcement Learning

2025-12-18T12:00:00Z•10 min read

#reinforcement learning #deep learning #goal-conditioned rl #network architecture #self-supervised learning

Recent research shows 1024-layer networks achieve 2x to 50x improvements in goal-conditioned RL. Here's why extreme depth works now, and when you should consider it for your own agents.

DiscoRL: When Algorithms Learn to Design Algorithms

2025-12-08•15 min read

#reinforcement learning #meta-learning #deepmind #algorithm discovery #ai research #machine learning

DeepMind's DiscoRL discovers reinforcement learning algorithms that outperform hand-designed methods like PPO and DQN. By treating algorithm design as a meta-learning problem, it found alternatives to value functions and bootstrapping through optimization alone.

When Machines Design Their Own Learning Algorithms

2025-12-08•13 min read

#reinforcement learning #meta-learning #algorithm discovery #deep learning #ai research #machine learning

A machine trained on simple grid worlds beat every hand-designed RL algorithm on Atari. DeepMind's DiscoRL discovers algorithms through meta-learning that outperform DQN, PPO, and A3C - methods humans spent decades developing.

Biology's Secret Weapon: Physics-Based Benchmarks for Training RL Agents

2025-12-03•16 min read

#reinforcement learning #biological benchmarks #protein design #rna design #computational biology #machine learning #alphafold #drug discovery

Why biological systems offer the ideal training ground for reinforcement learning: automated verification through physics, not human judgment. From protein design with AlphaFold to RNA folding with ViennaRNA, biology provides the verifiable inverse problems that RL needs at scale.

What Are World Models? The AI Architecture That Learns to Dream

2025-11-13•22 min read

#ai #machine learning #reinforcement learning #world models #robotics #autonomous vehicles

World models enable AI agents to imagine futures and plan actions, achieving 10-100x better sample efficiency than traditional reinforcement learning. From DreamerV3 collecting diamonds in Minecraft to foundation models like Sora and Genie, world models represent AI's shift from pattern matching to simulating reality itself.

DeepAgent: Teaching AI Agents to Remember and Learn

2024-10-28T17:58:48Z•15 min read

#ai #machine learning #ai agents #reinforcement learning #memory systems

DeepAgent bridges the gap between research and production AI agents with autonomous memory folding and reinforcement learning that scales from tens to thousands of tools.

Teaching AI to Keep Buildings Standing: Reinforcement Learning and Physics-Informed Design

2024-07-08T12:54:48+01:00•6 min read

#ai #reinforcement learning #architecture #structural engineering #physics #simulation #pidrl #piml

Exploring how Reinforcement Learning (RL) combined with Physics-Informed Machine Learning (PIML) can teach AI to design structurally sound and resilient buildings by learning from simulated physical environments.