Any plans on evaluating RETRO (the retrieval augmented transformer from DeepMind) on TruthfulQA? I'm guessing it should perform similarly to WebGPT but would be nice to get a concrete number.
Great post! I think you might wanna emphasize just how crucial ReAnalyse is for data-efficiency (the default MuZero is quite sample in-efficient), and how the reanalyse-ratio can be tuned easily for any data budget using a log-linear scaling law. You can also interpret the off-policy correction thing as running ReAnalyse twice, so my TL;DR of EfficientZero would be "MuZero ReAnalyse + SPR".
Regarding contrastive vs SPR, I don't think you would find a performance boost using a contrastive loss compared to SPR on Atari at least. We did an ablation...
Thanks, glad you liked it, I really like the recent RL directions from OpenAI too! It would be interesting to see the use of model-based RL for the "RL as fine-tuning paradigm": making large pre-trained models more aligned/goal-directed efficiently by simply searching over a reward function learned from humans.
I was eyeballing Figure 2 in the PPG paper and comparing it to our results on the full distribution (Table A.3).
PPO: ~0.25
PPG: ~0.52
MuZero: 0.68
MuZero+Reconstruction: 0.93
The Q-Learning baseline is a model-free control of MuZero. So it shares implementation details of MuZero (network architecture, replay ratio, training details etc.) while removing the model-based components of MuZero (details in sec A.2) . Some key differences you'd find vs a typical Q-learning implementation:
They do seem to cover SPR (an earlier version of SPR was called MPR). @flodorner If you do decide to update the plot, maybe you could update the label as well?
We do actually train/evaluate on the full distribution (See Figure 5 rightmost). MuZero+SSL versions (especially reconstruction) continue to be a lot more sample-efficient even in the full-distribution, and MuZero itself seems to be quite a bit more sample efficient than PPO/PPG.
Worth noting that they already use BERT in Search. https://blog.google/products/search/search-language-understanding-bert/
The raw neural network does use search during training though, and does not rely on search only during evaluation.
My main takeaway from Gato: If we can build specialized AI agents for 100s/1000s of tasks, it's now pretty straightforward to make a general agent that can do it all in a single model. Just tokenize data from all the tasks and feed into a transformer.