Search Results for author:
Found 14 papers, 5 papers with code
Many-Shot In-Context Learning
Finally, we demonstrate that, unlike few-shot learning, many-shot learning is effective at overriding pretraining biases and can learn high-dimensional functions with numerical inputs.
Guided Evolution with Binary Discriminators for ML Program Search
How to automatically design better machine learning programs is an open problem within AutoML.
Beyond Human Data: Scaling Self-Training for Problem-Solving with Language Models
To do so, we investigate a simple self-training method based on expectation-maximization, which we call ReST$^{EM}$, where we (1) generate samples from the model and filter them using binary feedback, (2) fine-tune the model on these samples, and (3) repeat this process a few times.
Improving Large Language Model Fine-tuning for Solving Math Problems
With these methods, we present a thorough empirical study on a series of PaLM 2 models and find: (1) The quality and style of the step-by-step solutions used for fine-tuning can make a significant impact on the model performance; (2) While solution re-ranking and majority voting are both effective for improving the model performance when used separately, they can also be used together for an even greater performance boost; (3) Multi-task fine-tuning that sequentially separates the solution generation and evaluation tasks can offer improved performance compared with the solution fine-tuning baseline.
Small-scale proxies for large-scale Transformer training instabilities
In this work, we seek ways to reproduce and study training stability and instability at smaller scales.
Evolving Pareto-Optimal Actor-Critic Algorithms for Generalizability and Stability
Generalizability and stability are two key objectives for operating reinforcement learning (RL) agents in the real world.
Information is Power: Intrinsic Control via Information Capture
We study this question in dynamic partially-observed environments, and argue that a compact and general learning objective is to minimize the entropy of the agent's state visitation estimated using a latent state-space model.
Differentiable Architecture Search for Reinforcement Learning
In this paper, we investigate the fundamental question: To what extent are gradient-based neural architecture search (NAS) techniques applicable to RL?
Evolving Reinforcement Learning Algorithms
Learning from scratch on simple classical control and gridworld tasks, our method rediscovers the temporal-difference (TD) algorithm.
Ecological Reinforcement Learning
Much of the current work on reinforcement learning studies episodic settings, where the agent is reset between trials to an initial state distribution, often with well-shaped reward functions.
Entity Abstraction in Visual Model-Based Reinforcement Learning
This paper tests the hypothesis that modeling a scene in terms of entities and their local interactions, as opposed to modeling the scene globally, provides a significant benefit in generalizing to physical tasks in a combinatorial space the learner has not encountered before.
Guiding Policies with Language via Meta-Learning
However, a single instruction may be insufficient to fully communicate our intent or, even if it is, may be insufficient for an autonomous agent to actually understand how to perform the desired task.
Self-Consistent Trajectory Autoencoder: Hierarchical Reinforcement Learning with Trajectory Embeddings
We show that we can learn continuous latent representations of trajectories, which are effective in solving temporally extended and multi-stage problems.
Hierarchical Reinforcement Learning
reinforcement-learning
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EX2: Exploration with Exemplar Models for Deep Reinforcement Learning
Deep reinforcement learning algorithms have been shown to learn complex tasks using highly general policy classes.
