Daphne Cornelisse @daphnesolves
(Multi-agent) simulation and RL • Ph.D. student NYU • Prev @Waymo @NVIDIAAI. daphne-cornelisse.com NYC Joined September 2017-
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I'm super bored of LLM-stylized papers/blogs. I think we all are? So I asked Claude to explain its tells: web.mit.edu/phillipi/www/c… Let's avoid these! I'd much rather read *your* words, see *your* rickety figures, than face down yet another of Claude's anodized corporate veneers.
The details on the sweep software can be found here: puffer.ai/blog/protein/ We basically swept all hyperparameters, you can checkout the craftax and default.ini in the repo. I haven't analyzed the winning hypers yet, but you can see them in the browser here puffer.ai/constellation.… -> click on craftax in the top left
Good question! I agree, human exploration seems more deliberate than most existing uncertainty-based RL algorithms. They capture the high-level goal of exploration (maximizing some measure of surprise), but can be brittle because it is difficult to distinguish between relevant and irrelevant variance. Still thinking on this as well, but here is a lecture with some of these topics in case it is helpful: drive.google.com/file/d/1sUhou6…
Very cool results and interesting write-up! A couple of thoughts: 1. At the core, this seems like an insanely difficult behavior prediction problem due to the quirk you mention. Given the limited information (i.e., you can't observe bullets), your success largely depends on your ability to accurately model your partner's behavior and exploit it. I'd expect modeling the opponent to play a larger role here than in, say, Chess. 2. Are you explicitly considering "diversity" (e.g., action dist entropy) in maintaining your pool of checkpoints? As you mentioned, the key here is probably figuring out how to make the policy explore a wide strategy space, but probably also which cpts to store.
i'd like to keep it low key and say this is no big deal, but in reality this has to be the coolest project i've worked on maybe ever, and there's a lot more to come! x.com/i/article/2099…
@aneeshers Thank you, Aneesh! Let me know if you have any thoughts!
Joseph and team have been pursuing a very different approach to AI compared to the mainstream. They build really fast simulators + optimizers — so fast that you can do RL from scratch, even on hard domains. It’s worth checking out! Very cool results!
Releasing PufferLib 5.0: Train agents in under a second
Interesting! Appreciate the detailed response. :) “e.g LLMs know a lot about Atari games right? could an LLM learn to play Atari really well with, say, 50-100k env steps of RL post-training?” > One thing I am trying to figure out for myself is: say you successfully do this. What have we learned then? How can this lead to an insight beyond “we can now post train LLMs on Atari”. Not trying to be pessimistic, but we often don’t even know what these models are (not) trained on 😔 There is definitely potential, but I feel that it can be challenging to turn such experiments into generally usable insights. I’m looking forward to learning more about your findings though!
Not yet! But is interesting to try. My intuition is that starting at different states will 1) improve sample complexity; 2) increase the reachability of achievements that have not been unlocked. It is not obvious to me it will enable the agent to solve the fire realm, since the exploration bonus is still unstructured.
The agent observation consists of a partial view of the env, full inventory and info about the agent itself (health, energy). As for my motivation, it was basically all of the above. I wanted to improve my systems knowledge and am interested in improving on-policy RL algorithms. Craftax is considered a difficult benchmark, so it seemed like a good exercise to work through the details and understand where PPO fails and why.
@creus_roger Thanks! I definitely share your excitement about the potential of providing human-like priors. I’m actually curious: what do you hope to achieve/learn in Craftax or other envs when experimenting along these lines?
@creus_roger @jsuarez @mitrma >Astra solved it by REALLY emphasizing that it MUST always build shelter before sleeping. we didn't have to do this :)
@jsuarez @spenccheng @finlay_sanders @ValtteriValo @elliotarledge Pause what you are doing - Pufferlib 5.0 is out! A little write up on Craftax here: x.com/daphnesolves/s…
Releasing PufferLib 5.0: Train agents in under a second
5 Exciting new results brought to you by our team + contributors: @spenccheng @finlay_sanders (fintern!) @ValtteriValo @daphnesolves @elliotarledge. Play with the agents at puffer.ai! All experimental data available is available in Constellation online + local.
terrytao.wordpress.com/2026/09/11/a-s… Terry’s essay is interesting because it highlights a misalignment between AI companies and many, if not most, social groups in humanity Many of our institutions are built around improving humanity’s understanding of how the world works and how to create things within it AI companies like OpenAI are more concerned with producing artifacts that have historically been challenging for people to create They don’t really care about helping people understand the world On the one hand, its a fair point that if we can solve a key problem, like finding a cure for cancer, it shouldn’t matter if we understand the solution on the other hand, this is a deeply antisocial approach to discovery and one that is increasingly leaving the world — artists, writers, now mathematicians — with animosity towards AI Rather than build AI as a tool to solve arbitrary problems, we could build it as a tool to empower people as they learn and perform tasks in the world Some companies, eg @percepta, are taking this approach. I hope more will follow suit
Brené Brown’s books on leadership are my new audiobook addiction
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