I made a superhuman Generals.io agent with self-play RL [P]
Summary
Trained a superhuman Generals.io agent using self-play reinforcement learning with a JAX-based pipeline and Vision Transformer. Achieved #1 on human 1v1 leaderboard; all code and a fast JAX simulator open-sourced.
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@reach_vb: https://x.com/reach_vb/status/2057880274348695995
A user demonstrates using OpenAI's Codex to automatically generate a Colab notebook that trains a ~10 million parameter transformer in JAX/Flax/Optax on addition, achieving high accuracy after 4000 steps on a T4 GPU.
Self-play helped AI achieve superhuman performance in Go, so why hasn’t it done the same for LLMs? Researchers have found a solution.
Researchers introduce Self-Guided Self-Play (SGS), a self-play algorithm for LLMs that prevents reward hacking by using a Guide role to score synthetic problems. Applied to theorem proving in Lean4, SGS surpasses RL baselines and allows a 7B model to outperform a 671B model.
@dair_ai: // Self-play with a pinch of human data // Really cool paper combining human demonstrations and self-play RL. 30 minute…
A research paper that combines a small amount of human demonstrations as a regularization objective with self-play reinforcement learning, enabling human-compatible driving policies using far less human data (30 minutes vs thousands of hours) and training in 15 hours on a single consumer GPU.
@browser_use: Agents playing games online? We asked our v4 agent to play powerline[.]io > Analyzed the game state and objective > Cre…
browser_use demonstrates their v4 AI agent autonomously playing the online game powerline.io by analyzing the game state and creating a real-time subagent to compete for first place.
@neural_avb: This is what you can achieve with 5-6 hours of Self-Play RL training by the way Actors view the projectiles with lidar …
A thread sharing a video of self-play RL training with lidar and PPO in Unity, followed by a lecture on building AlphaGo from scratch.