Claude-3.7 was tested on Pokémon Red, but what about more real-time games like Super Mario 🍄🌟?
We threw AI gaming agents into LIVE Super Mario games and found Claude-3.7 outperformed other models with simple heuristics. 🤯
Claude-3.5 is also strong, but less capable of planning complex maneuvers. Gemini-1.5-pro and GPT-4o perform less well.
We built Gaming agents to run platformers and puzzle video games in real time. Check out our demos and try our repo yourself to customize your own gaming agent! 🎮
In addition to Super Mario Bros, we also support 2048, as well as Tetris. More games are coming soon! 👾github.com/lmgame-org/Gam…
In addition to the classics, our LMGames team also designs and hosts computer games for AI evaluations.
Our mission is to study new perspectives for AI evaluations and the evolving roles humans play in evaluations.
We believe games provide challenging and dynamic environments for testing LLM agents.
Reasoning models often waste tokens self-doubting.
Dynasor saves you up to 81% tokens to arrive at the correct answer! 🧠✂️
- Probe the model halfway to get the certainty
- Use Certainty to stop reasoning
- 100% Training-Free, Plug-and-play
[2/n] Observation: Reasoning models (🟠) use WAY more tokens than needed vs traditional models (🔵).
Although reasoning models achieve higher acc%, they consume much more tokens than traditional models. User who can accept a lower acc% will waste tons more money💰💰.
Why?
[3/n] 🐢 Reasoning model usually self-doubt.
Model only spends 300 tokens arriving at the right answer, but spends the extra 990 tokens on meaningless verification loops, making no progress at all!
➡️ "Wait, is 2+2 really 4? Let me check..."