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Found 20 papers, 6 papers with code
That Chip Has Sailed: A Critique of Unfounded Skepticism Around AI for Chip Design
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.
h4rm3l: A language for Composable Jailbreak Attack Synthesis
We demonstrate h4rm3l's efficacy by synthesizing a dataset of 2656 successful novel jailbreak attacks targeting 6 SOTA open-source and proprietary LLMs, and by benchmarking those models against a subset of these synthesized attacks.
RAPTOR: Recursive Abstractive Processing for Tree-Organized Retrieval
Retrieval-augmented language models can better adapt to changes in world state and incorporate long-tail knowledge.
Ranked #3 on
Question Answering
on QuALITY
Specific versus General Principles for Constitutional AI
Constitutional AI offers an alternative, replacing human feedback with feedback from AI models conditioned only on a list of written principles.
The Capacity for Moral Self-Correction in Large Language Models
We believe that at this level of scale, language models obtain two capabilities that they can use for moral self-correction: (1) they can follow instructions and (2) they can learn complex normative concepts of harm like stereotyping, bias, and discrimination.
Constitutional AI: Harmlessness from AI Feedback
In the RL phase, we sample from the finetuned model, use a model to evaluate which of the two samples is better, and then train a preference model from this dataset of AI preferences.
Measuring Progress on Scalable Oversight for Large Language Models
Developing safe and useful general-purpose AI systems will require us to make progress on scalable oversight: the problem of supervising systems that potentially outperform us on most skills relevant to the task at hand.
Delving into Macro Placement with Reinforcement Learning
In physical design, human designers typically place macros via trial and error, which is a Markov decision process.
A Full-Stack Search Technique for Domain Optimized Deep Learning Accelerators
The rapidly-changing deep learning landscape presents a unique opportunity for building inference accelerators optimized for specific datacenter-scale workloads.
Transferable Graph Optimizers for ML Compilers
Most compilers for machine learning (ML) frameworks need to solve many correlated optimization problems to generate efficient machine code.
Chip Placement with Deep Reinforcement Learning
To achieve these results, we pose placement as a Reinforcement Learning (RL) problem and train an agent to place the nodes of a chip netlist onto a chip canvas.
Placement Optimization with Deep Reinforcement Learning
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 objective, subject to constraints.
TPO: TREE SEARCH POLICY OPTIMIZATION FOR CONTINUOUS ACTION SPACES
In our experiments, we use PPO as our baseline policy optimization algorithm.
Generalized Clustering by Learning to Optimize Expected Normalized Cuts
We introduce a novel end-to-end approach for learning to cluster in the absence of labeled examples.
GDP: Generalized Device Placement for Dataflow Graphs
Runtime and scalability of large neural networks can be significantly affected by the placement of operations in their dataflow graphs on suitable devices.
Policy Optimization by Local Improvement through Search
On the other end of the spectrum, approaches rooted in Policy Iteration, such as Dual Policy Iteration do not choose next step actions based on an expert, but instead use planning or search over the policy to choose an action distribution to train towards.
GAP: Generalizable Approximate Graph Partitioning Framework
Graph partitioning is the problem of dividing the nodes of a graph into balanced partitions while minimizing the edge cut across the partitions.
Ranked #1 on
graph partitioning
on custom
A Hierarchical Model for Device Placement
We introduce a hierarchical model for efficient placement of computational graphs onto hardware devices, especially in heterogeneous environments with a mixture of CPUs, GPUs, and other computational devices.
Massive Exploration of Neural Machine Translation Architectures
Neural Machine Translation (NMT) has shown remarkable progress over the past few years with production systems now being deployed to end-users.
Generating High-Quality and Informative Conversation Responses with Sequence-to-Sequence Models
Sequence-to-sequence models have been applied to the conversation response generation problem where the source sequence is the conversation history and the target sequence is the response.
