muzero-general VS trax

I went looking for MuZero implementations in order to see how, exactly, they interact with the game space. Based on this one, which had the most stars in the muzero topic, it appears that it needs to be able to discern legal next steps from the current game state https://github.com/werner-duvaud/muzero-general/blob/master/...

So, I guess for the cards Forge has implemented one could MuZero it, but I believe it's a bit chicken and egg with a "free text" game like M:TG -- in order to train one would need to know legal steps for any random game state, but in order to have legal steps one would need to be able to read and interpret English rules and card text

You may want to use the trick described in https://arxiv.org/pdf/1805.11593.pdf as a Transformed Bellman Operator. Its efficiency is proved in MuZero original paper https://arxiv.org/pdf/1911.08265.pdf Appendix F. The implementation of that method you can find here: https://github.com/werner-duvaud/muzero-general Usage: muzero/models.py:649 (def support_to_scalar)

I have used this implementation from MuZero: https://github.com/werner-duvaud/muzero-general

+1 to taking a look at OpenSpiel. It has AlphaZero in C++ and Python, and there is even a PR open that allows running UCI (e.g. Stockfish) bot. You can also load chess via the OpenSpiel wrapper in muzero-general: https://github.com/werner-duvaud/muzero-general

When I looked up some community implementations, like that of Werner Duvaud on GitHub and Discord, hoping to make my own contributions to this effect, I soon found that I was hopelessly out of my depth as an amateur programmer, even with the help of some other sources like this walkthrough series. However, from what I could tell, most of the people working on this sort of thing seemed to be tackling relatively simple games. At first I thought this might be largely due to limitations in hobby time or computing power available to these users, but then I also noticed that, unless I have misunderstood something, it seems like the games are required to be rebuilt entirely in the engine of (this implementation of) MuZero, which would also obviously be a limit on the complexity of games chosen.

Windows support (Experimental / Workaround: Use the notebook in Google Colab)

There are several if you google "muzero github", e.g. https://github.com/werner-duvaud/muzero-general

Is t5x an encoder/decoder architecture?

Some more general options.

The Flax ecosystem

https://github.com/google/flax?tab=readme-ov-file

or dm-haiku

https://github.com/google-deepmind/dm-haiku

were some of the best developed communities in the Jax AI field

Perhaps the “trax” repo? https://github.com/google/trax

Some HF examples https://github.com/huggingface/transformers/tree/main/exampl...

Sadly it seems much of the work is proprietary these days, but one example could be Grok-1, if you customize the details. https://github.com/xai-org/grok-1/blob/main/run.py

and the implementation https://github.com/google/trax/blob/master/trax/models/resea... if you are interested.

Hope you get to look into this!

Thank you for developing the pipeline and amassing considerable compute for gathering and preprocessing this dataset!

I'm not sure if this is the right place to ask about this, but could you consider training an LLM using a more advanced, sparse transformer architecture (specifically, "Terraformer" from this paper https://arxiv.org/abs/2111.12763 and this codebase https://github.com/google/trax/blob/master/trax/models/resea... by Google Brain and OpenAI)? I understand the pressure to focus on training a straightforward LLaMA replication, but of course you see that it's a legacy dense architecture which limits its inference performance. This new architecture is not just an academic curiosity but is already validated at scale by Google, providing 10x+ inference performance boost on the same hardware.

Frankly, the community's compute budget - for training and for inference - isn't infinite, and neither is the public's interest in models that do not have advantage (at least in convenience) over closed-source ones; and so we should utilize both those resources as efficiently as possible. It could be a big step forward if you trained at least LLaMA-Terraformer-7B and 13B foundation models on the whole dataset.

But a couple years later they came out with open source implementations yeah: https://github.com/google/trax/tree/master/trax/models/reformer

Code: https://github.com/google/trax/blob/master/trax/examples/Terraformer_from_scratch.ipynb

Training language models is expensive. Train the biggest model you can afford. I assume you've tried the colab from the reformer GitHub: https://github.com/google/trax/tree/master/trax/models/reformer