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@giffmana

With first Claude and now Gemini playing Pokemon, I was thinking of doing my own game-playing experiment over the weekend. However, I quickly learned that it's very far from the VLA-style "pixels->plan" that I naively thought it was, and wanted to do myself. It's like 90%… https://t.co/5cVrbmYArc

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                              "full_text": "@giffmana I respect you immensely @giffmana, but this hit piece on a live stream is a miss for me - honestly, I consider these just interesting Gemini marketing way more entertaining than fake hypes in some empty tweets that other lab employees generates. Nobody said it’s raw pixels, it’s…",
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                              "full_text": "@programmer_dude yes totally, I like both projects, they seem like a lot of fun!\n\nI don't like how they are used (by others, not the streamers) for marketing, and indirectly misleading a lot of researchers like myself.",
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                                  "text": "Thanks for sharing! Yeah generally I think the Claude / Gemini plays Pokemon examples are similar to some of the earlier SC2 & Dota RL agents that were developed in that they’re given a lot of external information.\n\nThey’re definitely still very interesting, but they’re also evaluating something different than just raw VLM reasoning capabilities and the ability to ground the game screen to language properly. But I’m also somewhat confident that these settings are so much simpler / easier than topping SC2 / Dota that just the game screen should be sufficient.\n\nFunnily enough, even just the simple grid overlay with coordinates that Anthropic / GDM are providing are extremely useful. When we ran our really basic VLM agents on grid-world GBA games like Pokemon, Zelda, etc. the models really struggled to understand how to properly move the game characters around or even determine what is a “walkable” terrain and often just got stuck in these weird loops of doing nothing in basic areas of the game!",
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                              "full_text": "@Clad3815 Not saying this is cheated, and what is \"ok\" and what is \"not ok\" completely depends on what one is doing. For just having fun this is perfectly fine.\n\nBut the way this is made to look like a static benchmark and, which it's absolutely not. And as you can see in my replies, many…",
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                              "full_text": "@giffmana Generalisation is building the game and then playing the game\n\nIt's futile for AGI gamebenchmark if it doesn't build the game first (yes humans retrospectively understand gamebuild by playing it) \n\nSo far only claude3.7 can replicate pokemon gameboy games in code ≈ 70%",
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                                  "text": "Agreed. LLMs are surprisingly bad in ASCII roguelikes or gym envs too. I say \"surprisingly\", because there's certainly that sort of data in the pretraining set, so while I can kinda understand pixels -> actions failing (there's just nothing in pretraining for that), I'd have expected better performance on Nethack and such. Hell, even SOTA models still struggle with tic-tac toe, or that simple taxi env in gymnasium (can't even get grid positions in a 5x5 grid right)\n\nSomething is clearly missing. Could just be as simple as a robust finetune (that demo of Llama-7b playing doom with ASCII graphics showed it's maybe enough). Working on it",
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                              "full_text": "@giffmana Agreed. LLMs are surprisingly bad in ASCII roguelikes or gym envs too. I say \"surprisingly\", because there's certainly that sort of data in the pretraining set, so while I can kinda understand pixels -&gt; actions failing (there's just nothing in pretraining for that), I'd have…",
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                                  "text": "It actually heavily depends on the game. Both models know Pokemon by heart already (training data) and they know exactly what they need to do next at every given observation. The main reason you still have the harness is efficiency, especially in navigation. I‘m very sure both models could play almost the entirety of the game fully black box at this point, but it would be unwatchable since 90% of it would just be trying to slowly get from point A to B (and also there will be some reaction-based interactions they would probably not be able to perform). (Source: we‘ve literally done the exact same demo and benchmark one year ago already, its on our website)",
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