334 citations · 1.4k across the 54 of their papers we have counts for
12 papers · 1 filter
Sharpening Tax in Post-Training
Changdae Oh, Qi Zeng, Qi Qi +7
An emerging hypothesis about reinforcement learning (RL) post-training of large language models (LLMs) is that it merely sharpens existing behaviors of a base model, improving sing…
AI as a Compiler: Compiling Triton kernels without the Triton compiler
François Costa, Charly Castes, Thomas Bourgeat +1
Compiler backends are expensive to build and maintain as programming models, workloads, and accelerators evolve. We investigate whether large language models can replace the conven…
LLM-as-a-Verifier: A General-Purpose Verification Framework
Jacky Kwok, Shulu Li, Pranav Atreya +6
Scaling pre-training, post-training, and test-time compute have become the central paradigms for improving the capabilities of LLMs. In this work, we identify verification, the abi…
TRACE: Capability-Targeted Agentic Training
Hangoo Kang, Tarun Suresh, Jon Saad-Falcon +1
Models often fail to complete agentic tasks because they lack core capabilities required by the target environment. However, mainstream approaches for addressing these failures typ…
AI+HW 2035: Shaping the Next Decade
Deming Chen, Jason Cong, Azalia Mirhoseini +27
Artificial intelligence (AI) and hardware (HW) are advancing at unprecedented rates, yet their trajectories have become inseparably intertwined. The global research community lacks…
On the Role of Temperature Sampling in Test-Time Scaling
Yuheng Wu, Azalia Mirhoseini, Thierry Tambe
Large language models (LLMs) can improve reasoning at inference time through test-time scaling (TTS), where multiple reasoning traces are generated and the best one is selected. Pr…
ForTIFAI: Fending Off Recursive Training Induced Failure for AI Model Collapse
Soheil Zibakhsh Shabgahi, Pedram Aghazadeh, Azalia Mirhoseini +1
The increasing reliance on generative AI models is rapidly increasing the volume of synthetic data, with some projections suggesting that most available new data for training could…
SPRINT: Enabling Interleaved Planning and Parallelized Execution in Reasoning Models
Emil Biju, Shayan Talaei, Zhemin Huang +3
Large reasoning models (LRMs) excel at complex reasoning tasks but typically generate lengthy sequential chains-of-thought, resulting in long inference times before arriving at the…
Synthetic Data Generation & Multi-Step RL for Reasoning & Tool Use
Anna Goldie, Azalia Mirhoseini, Hao Zhou +2
Reinforcement learning has been shown to improve the performance of large language models. However, traditional approaches like RLHF or RLAIF treat the problem as single-step. As f…
How Do Large Language Monkeys Get Their Power (Laws)?
Rylan Schaeffer, Joshua Kazdan, John Hughes +7
Recent research across mathematical problem solving, proof assistant programming and multimodal jailbreaking documents a striking finding: when (multimodal) language model tackle a…
That Chip Has Sailed: A Critique of Unfounded Skepticism Around AI for Chip Design
Anna Goldie, Azalia Mirhoseini, Jeff Dean
In 2020, we introduced a deep reinforcement learning method capable of generating superhuman chip layouts, which we then published in Nature and open-sourced on GitHub. AlphaChip h…
Placement Optimization with Deep Reinforcement Learning
Anna Goldie, Azalia Mirhoseini
Placement Optimization is an important problem in systems and chip design, which consists of mapping the nodes of a graph onto a limited set of resources to optimize for an objecti…