As always, thanks to everyone involved for the newsletter! I’m usually particularly interested in the other RL/Deep Learning papers, as those are the ones I have less chance to find on my own.
On this newsletter, I especially enjoyed the summaries and opinions about the two scaling papers, and the comparison between the two.
As always, thanks to everyone involved for the newsletter! I’m usually particularly interested in the other RL/Deep Learning papers, as those are the ones I have less chance to find on my own.
On this newsletter, I especially enjoyed the summaries and opinions about the two scaling papers, and the comparison between the two.