We sent a swarm of AI agents to solve Karpathyโs NanoChat benchmark, and they crushed SoTA in 3 days.
We built our own harness on top of a graph database to do auto-autoresearch, each iteration learning from mistakes to do research better.
The team wrote >15,000 entries. ๐งต
Most agent demos stop working when you close the chat. Ours accumulates knowledge even when we sleep.
@venkatGoku used our OSS autoresearch harness to research a better C++ factory scheduler. It beat an expert-designed solver on 29/30 fresh problems and runs with no LLM.
The solver, tests and research record are open source.
https://t.co/zkG6leIWkD
Join our Discord, we collaborate with scientists here to make this open research possible: https://t.co/NfwLkztRWA
In one case, figured out how to use 10 trucks instead of 30. The agent's solver found a legal plan the reference never considered.
Built with agent harness `sagent` and knowledge graph `trackinizer`:
https://t.co/a8bWTDBI2i
https://t.co/9HDLKFHnyd
We're introducing TasteBench, a new standard to measure Research Taste on real world ML tasks.
Existing benchmarks have limited datasets and eval data ultimately becomes part of training data. In TasteBench, we continuously generate new ML tasks on OOS data no model has trained on.
Experts doubted a small neural net could beat Craftax without an LLM's knowledge.
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Our agent meta-harness built one in 3 weeks. The 15M-param agent beats the final boss 44% of the time, costing a cent, and 10 seconds.
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GPT-6 Astra won 1%, taking 24 hours and $83. ๐งต
rekursiv at every level: agents run the research, the learner restarts from its own best moments, and each generation's games train the model that teaches the next.
๐ Full story: https://t.co/Z1r0WDq4WE
๐ฌ Join our Discord: https://t.co/NfwLkztk72
We got agents to train a 15M-param RL agent which solves Craftax, a 2D Minecraft-inspired game.
Here are 5 fun expert-level strategies the model learned, all on its own. We OSSed the model trainer on github.
1. It jails monsters in tiny stone cells and leaves them there.
Experts doubted a small neural net could beat Craftax without an LLM's knowledge.
โ
Our agent meta-harness built one in 3 weeks. The 15M-param agent beats the final boss 44% of the time, costing a cent, and 10 seconds.
โ
GPT-6 Astra won 1%, taking 24 hours and $83. ๐งต
@Taodav The improvements proposed by the agents were fairly general, not task specific. E.g., representing the input board as a 2D grid and using CNNs. The agent mentioned it was inspired was inspired by works like VARC https://t.co/WGkHlssNbY
Huge shoutout to @daphnesolves and @puffer_ai for their work on PufferLib, which is an awesome piece of software! It's what inspired us to try the challenge. And big thanks to @creus_roger for writing out a detailed summary of GPT-6 Astra solving Craftax.
Experts doubted a small neural net could beat Craftax without an LLM's knowledge.
โ
Our agent meta-harness built one in 3 weeks. The 15M-param agent beats the final boss 44% of the time, costing a cent, and 10 seconds.
โ
GPT-6 Astra won 1%, taking 24 hours and $83. ๐งต
The "3 weeks" here is also a bit conservative. The campaign actually beat the game in a few days, the rest of the time was spent performing ablations to improve the score, and get the meta-harness to discover their ideas faster.