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Found 18 papers, 5 papers with code
Hallucination Detection on a Budget: Efficient Bayesian Estimation of Semantic Entropy
First, due to us taking the Bayesian approach, we achieve a much better quality of semantic entropy estimates for a given budget of samples from the LLM.
Learning in complex action spaces without policy gradients
This raises the question of why their computational applicability and performance diverge as the complexity of the action space increases.
Soft Preference Optimization: Aligning Language Models to Expert Distributions
We propose Soft Preference Optimization (SPO), a method for aligning generative models, such as Large Language Models (LLMs), with human preferences, without the need for a reward model.
On the Importance of Uncertainty in Decision-Making with Large Language Models
We compare this baseline to LLM bandits that make active use of uncertainty estimation by integrating the uncertainty in a Thompson Sampling policy.
In-context Exploration-Exploitation for Reinforcement Learning
In-context learning is a promising approach for online policy learning of offline reinforcement learning (RL) methods, which can be achieved at inference time without gradient optimization.
Auxiliary task discovery through generate-and-test
In this paper, we explore an approach to auxiliary task discovery in reinforcement learning based on ideas from representation learning.
Importance Sampling Placement in Off-Policy Temporal-Difference Methods
A central challenge to applying many off-policy reinforcement learning algorithms to real world problems is the variance introduced by importance sampling.
An Empirical Comparison of Off-policy Prediction Learning Algorithms in the Four Rooms Environment
In the Rooms task, the product of importance sampling ratios can be as large as $2^{14}$ and can sometimes be two.
An Empirical Comparison of Off-policy Prediction Learning Algorithms on the Collision Task
In the middle tier, the five Gradient-TD algorithms and Off-policy TD($\lambda$) were more sensitive to the bootstrapping parameter.
Does the Adam Optimizer Exacerbate Catastrophic Forgetting?
Catastrophic forgetting remains a severe hindrance to the broad application of artificial neural networks (ANNs), however, it continues to be a poorly understood phenomenon.
From Eye-blinks to State Construction: Diagnostic Benchmarks for Online Representation Learning
We present three new diagnostic prediction problems inspired by classical-conditioning experiments to facilitate research in online prediction learning.
Gradient Temporal-Difference Learning with Regularized Corrections
It is still common to use Q-learning and temporal difference (TD) learning-even though they have divergence issues and sound Gradient TD alternatives exist-because divergence seems rare and they typically perform well.
Improving Performance in Reinforcement Learning by Breaking Generalization in Neural Networks
Unfortunately, the performance of deep reinforcement learning systems is sensitive to hyper-parameter settings and architecture choices.
Overcoming Catastrophic Interference in Online Reinforcement Learning with Dynamic Self-Organizing Maps
Yet, neural networks tend to forget what they learned in the past, especially when they learn online and fully incrementally, a setting in which the weights are updated after each sample is received and the sample is then discarded.
Should All Temporal Difference Learning Use Emphasis?
ETD was proposed mainly to address convergence issues of conventional Temporal Difference (TD) learning under off-policy training but it is different from conventional TD learning even under on-policy training.
Online Off-policy Prediction
The ability to learn behavior-contingent predictions online and off-policy has long been advocated as a key capability of predictive-knowledge learning systems but remained an open algorithmic challenge for decades.
Two geometric input transformation methods for fast online reinforcement learning with neural nets
We apply neural nets with ReLU gates in online reinforcement learning.
A First Empirical Study of Emphatic Temporal Difference Learning
In this paper we present the first empirical study of the emphatic temporal-difference learning algorithm (ETD), comparing it with conventional temporal-difference learning, in particular, with linear TD(0), on on-policy and off-policy variations of the Mountain Car problem.
