Representation matters. Representation matters. Representation matters, even for generative models. We might've been training our diffusion models the wrong way this whole time. Meet REPA: Training Diffusion Transformers is easier than you think! sihyun.me/REPA/(🧵1/n)

Excited to see that SpiritLM is fully open-sourced now. It supports speech and text as both input and output. Please consider trying it at: github.com/facebookresear…

Very happy to hear that GANs are getting the test of time award at NeurIPS 2024. The NeurIPS test of time awards are given to papers which have stood the test of the time for a decade. I took some time to reminisce how GANs came about and how AI has evolve in the last decade.

I have written a blogpost offering an explanation of why both the chosen and the rejected log-probability decreases during DPO, and more interestingly, why it is a desired phenomenon to some extent. Link: tianjianl.github.io/blog/2024/dpo/

I had a great time helping host MASC-SLL at Hopkins last year. MASC-SLL is a great opportunity to connect with fellow AI/NLP/Speech researchers. If your organization is in the Mid-Atlantic region and is interested in hosting the event, please reach out!

Congratulations to Prof. Philipp Koehn on being named a Fellow of the ACL 2025! cs.jhu.edu/news/philipp-k…

🛠️ DeepSeek-R1: Technical Highlights 📈 Large-scale RL in post-training 🏆 Significant performance boost with minimal labeled data 🔢 Math, code, and reasoning tasks on par with OpenAI-o1 📄 More details: github.com/deepseek-ai/De… 🐋 4/n

Making LLMs run efficiently can feel scary, but scaling isn’t magic, it’s math! We wanted to demystify the “systems view” of LLMs and wrote a little textbook called “How To Scale Your Model” which we’re releasing today. 1/n

Our latest on compressed representations: Key-Value Distillation (KVD). Query-independen transformer compression, with offline supervised distillation.

🚀 Excited to finally share our paper on VerlTool, released today after months of work since the initial release in late May! VerlTool is a high-efficiency, easy-to-use framework for Agentic RL with Tool use (ARLT), built on top of VeRL. It currently supports a wide range of

🌀Diversity Aware RL (DARLING)🌀 📝: arxiv.org/abs/2509.02534 - Jointly optimizes for quality & diversity using a learned partition function - Outperforms standard RL in quality AND diversity metrics, e.g. higher pass@1/p@k - Works for both non-verifiable & verifiable tasks 🧵1/5

Most RL for LLMs today is single-step optimization on a given state (e.g., an instruction), which is essentially a bandit setup. But to learn a meta-policy that can solve various bandit problems via in-context trial and error, you need true multi-turn RL over a long horizon. So,

Exploration is fundamental to RL. Yet policy gradient methods often collapse: during training they fail to explore broadly, and converge into narrow, easily exploitable behaviors. The result is poor generalization, limited gains from test-time scaling, and brittleness on tasks