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# 7/23/23 - Towards A Unified Agent with Foundation Models
https://arxiv.org/abs/2307.09668
Generate synthetic data set for the state that you want, search over the action space until you find a trajectory that reaches a cosine similarity threshold denoted by the state you want, add all those frames and states of the buffer and incorporate into training
You can bootstrap process with priors still search for the desired state
### reward
Reward any trajectory proportionally to a semantically similar state as any state in a run with a victory condition.
Linear or some function reward curve
### Sample curve
Sections of states with more changes in them
# 7/21/23
am going to naively, without evidence, state that you can represent any function in text with a large language model.
Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
# measured steps
Probably should have figured out sooner that small measured steps done in consistent application leads to results. Predicting the outcome while getting there can be interesting but is ultimately just an イメージ in your head.
# Stack More Layers Differently:
High-Rank Training Through Low-Rank Updates
https://arxiv.org/pdf/2307.05695.pdf
https://github.com/guitaricet/peft_pretraining |