Yiding Jiang @yidingjiang
PhD student @mldcmu @SCSatCMU. Formerly intern @MetaAI, AI resident @GoogleAI. BS from @Berkeley_EECS. Trying to understand stuff. yidingjiang.github.io Joined December 2015-
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1/What does it mean for an LLM to “memorize” a doc? Exactly regurgitating a NYT article? Of course. Just training on NYT?Harder to say We take big strides in this discourse w/*Adversarial Compression* w/@A_v_i__S @zhilifeng @zacharylipton @zicokolter 🌐:locuslab.github.io/acr-memorizati…🧵
1/ 🥁Scaling Laws for Data Filtering 🥁 TLDR: Data Curation *cannot* be compute agnostic! In our #CVPR2024 paper, we develop the first scaling laws for heterogeneous & limited web data. w/@goyalsachin007 @zacharylipton @AdtRaghunathan @zicokolter 📝:arxiv.org/abs/2404.07177
Check out this really cool work w/@yidingjiang ! We built a simple system (PCA + Clustering) for quantifying how "features" are distributed across models and data. Using this tool, we can mathematically understand the Generalization Disagreement Equality. 🤝
Check out this really cool work w/@yidingjiang ! We built a simple system (PCA + Clustering) for quantifying how "features" are distributed across models and data. Using this tool, we can mathematically understand the Generalization Disagreement Equality. 🤝
🚀Our latest blog post unveils the power of Consistency Models and introduces Easy Consistency Tuning (ECT), a new way to fine-tune pretrained diffusion models to consistency models. SoTA fast generative models using 1/32 training cost! 🔽 Get ready to speed up your generative…
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In the words of Billy Idol, give a "rebel yell" for REBEL: a strikingly simple RL algorithm (it's just regression! no clipping / critics!) that scales to generative models (both LLMs and Diffusion Models!) and has deep theoretical interpretations: arxiv.org/abs/2404.16767. [1/8]
There's been a lot of discussion on LLMs "memorizing" training data, but we argue for more nuance in the definition of "memorize". This work advocates for adversarial prompts (and whether they can be shorter than the output) as a metric for assessing memorization.
1/What does it mean for an LLM to “memorize” a doc? Exactly regurgitating a NYT article? Of course. Just training on NYT?Harder to say We take big strides in this discourse w/*Adversarial Compression* w/@A_v_i__S @zhilifeng @zacharylipton @zicokolter 🌐:locuslab.github.io/acr-memorizati…🧵
1/What does it mean for an LLM to “memorize” a doc? Exactly regurgitating a NYT article? Of course. Just training on NYT?Harder to say We take big strides in this discourse w/*Adversarial Compression* w/@A_v_i__S @zhilifeng @zacharylipton @zicokolter 🌐:locuslab.github.io/acr-memorizati…🧵
you were in my dreams last night brother. took me a moment to recognize it was you. happy birthday
To my wonderful brother-in-law Mat, you were a perfect father, husband, son, brother, and human. You will live on in our memories and through the four children to whom you gave the best possible start. I love and miss you.
Super excited to share that I successfully defended my PhD thesis "Understanding Generalization and Robustness in Modern Deep Learning" today 👨🎓 A huge thanks to the thesis examiners @SebastienBubeck, @zicokolter, and @KrzakalaF, jury president Rachid Guerraoui, and, of course,…
🌎 Excited to share a major update of the DreamerV3 agent! A couple of smaller changes, more benchmarks, and substantially improved performance. 👇 Main differences from our earlier preprint:
Excited to announce DreamerV3 🌍, a scalable and general RL algorithm that masters a wide range of applications with fixed hyperparameters! Applied out of the box, it solves the Minecraft Diamond challenge without human data. 💎 👇 Thread x.com/deepmind/statu…
Happy to share our work on preference learning methods for LLMs. Key insights: 1. Use more on-policy samples > off-policy samples 2. Contrastive DPO > Pref-FT. Also we provide insights on DPO's training mechanism. 3. Theoretical unification under mode-covering/seeking KL
Many LLM fine-tuning methods. Unclear what you should use & why? In our new paper, we did an extensive study of on-policy RL, supervised & offline contrastive methods (DPO, IPO) to answer this... 🧵⬇️ On-policy > offline, mode-seeking > mode-covering understanding-rlhf.github.io
Our work on @minerl_official was featured in this nice article about AI safety! Thanks @khulick for speaking with us.
It’s an ordinary day in Minecraft… until a bot starts destroying a house. How can we stop bad bot behavior? AI safety researchers are on the case. snexplores.org/article/artifi…
Reviewer’s evaluation of your argument
@Dahoas1 shares some cool insights on improving LLM reasoning with RL, highlighting exploration as one of the key challenges. Thanks @twimlai for discussing our work on your podcast!
Today we're joined by @Dahoas1 from @GeorgiaTech to discuss the reasoning capability of language models and the potential to improve it with traditional RL methods 🎧 / 🎥 Listen to the episode at: twimlai.com/go/680. 📖 CHAPTERS 00:00 - Introduction 02:19 - RL vs RLHF…
Llama-3 is absolutely impressive, but is it more resilient to adaptive jailbreak attacks compared to Llama-2? 🤔 Not much. The same approach as in our recent work arxiv.org/abs/2404.02151 leads to 100% attack success rate. The code and logs of the attack are now available:…
In machine learning, we often distinguish aleatoric and epistemic uncertainty. Aleatoric is called irreducible error because it's caused by "true" randomness, and epistemic is reducible caused by lack of information, like model misspecification. But it's not always clear
Can’t wait to see what you all build with Llama 3, the best openly available LLM! Enjoy and stay tuned for more🦙🦙🦙
Excited to share a preview of Llama3, including the release of an 8B and 70B (82 MMLU, should be the best open weights model!), and preliminary results for a 405B model (still training, but already competitive with GPT4). Lots more still to come... ai.meta.com/blog/meta-llam…
I passed my thesis proposal! 🎊Thanks to my amazing committee @fangf07, @hongshenus, Geoff Gordon, @katjahofmann, & @OriolVinyalsML for their feedback & support. & thank you to my friends and collaborators for waking up early today to attend 🖤
I am super excited to share our Llama3 preview models (8B and 70B). I am proud to have been a part of this amazing effort over the past 8 months. We still have some super cool stuff coming up in the coming months... until then, enjoy playing with these preview models…
My team and I are moving from Google Research to Google DeepMind. We'll keep working on SOTA LLM and multimodal models. Very excited for what's to come!
The Llama 3 models look fantastic! And we're proud to support them on Day 0 throughout NVIDIA's product stack: blogs.nvidia.com/blog/meta-llam…
We just released Meta Llama 3: the most capable openly available LLM available to date! The 8B & 70B models are out now, and we expect to release models with larger context windows, additional model sizes and more capabilities in the coming months.
Here is a grid of different 1D optimal transport solutions for different regularizations (columns) and unbalanced marginal penalizations (rows). The example is coming to POT shortly and is done with a for loops because we now implement all those solvers in one function (ot.solve)
Super excited to see this work out! Happy to have contributed a small part to thinking about the optimization dynamics at this scale :)
How to enjoy the best of both worlds of efficient training (less communication and computation) and inference (constant KV-cache)? We introduce a new efficient architecture for long-context modeling – Megalodon that supports unlimited context length. In a controlled head-to-head…
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