When humans need to learn a new skill, we can acquire knowledge through written books, including textbooks, tutorials, etc. However, current research for decision-making, like reinforcement learning (RL), has primarily required numerous real interactions with the target environment to learn a skill, while failing to utilize the existing knowledge already summarized in the text.

Large language models (LLMs) have achieved impressive performance across various natural language benchmarks, prompting a continual need to curate more difficult datasets for larger LLMs, which is costly and time-consuming. In this paper, we propose to automate dataset updating and provide systematical analysis regarding its effectiveness in dealing with benchmark leakage issue, difficulty control, and stability. Thus, once current benchmark has been mastered or leaked, we can update it for timely and reliable evaluation. There are two updating strategies: 1) mimicking strategy to generate similar samples based on original data, preserving stylistic and contextual essence, and 2) extending strategy that further expands existing samples at varying cognitive levels by adapting Bloom's taxonomy of educational objectives. Extensive experiments on updated MMLU and BIG-Bench demonstrate the stability of the proposed strategies and find that the mimicking strategy can effectively alleviate issues of overestimation from benchmark leakage. In cases where the efficient mimicking strategy fails, our extending strategy still shows promising results. Additionally, by controlling the difficulty, we can better discern the models' performance and enable fine-grained analysis -- neither too difficult nor too easy an exam can fairly judge students' learning status. To the best of our knowledge, we are the first to automate updating benchmarks for reliable and timely evaluation.

Female pastor is suspended after her shocking Epstein link is exposed... as she compares herself to JESUS while defending their relationship'Tell me to my face': Republican senator torches Noem's replacement as their vicious personal feud spills into public Outrageous full story of scandalous affair that's the talk of Manhattan's exclusive private schools: Family insiders reveal humiliating sex secrets... shock'confession' letter... and the furious relative who exposed it all Ugly new Nicole Kidman and Keith Urban divorce fight ERUPTS: Her friends share humiliating details of'midlife crisis'... and reveal brutal REAL reason daughter Sunday Rose'snubbed' him Perfect All-American family lived in stunning $1.1m Colorado mansion and bankrolled glamorous daughter's horse stables... now matriarch has sullied their good name with a HUGE scandal Meghan unveils new As Ever line with Lilibet... amid claims Netflix has been left with huge $10m surplus of her unsold products after'split' with streamer Woke Democrat, 26, who can't get out of bed in time for meetings loses primary to professor accused of inappropriate relationship by former student I watched the children's book author who poisoned her husband from 5ft away. This is the off-camera moment her mask finally slipped... it was truly chilling I ran America's only Supermax jail: What history's most notorious terrorists and serial killers told me as they waited to die Sinister truth about explosive resignation of Trump's top counter-terror chief Joe Kent... and his shock claim Israel is manipulating the president: MARK HALPERIN Hairdresser who weighs 300lbs says Southwest airport check-in worker looked him up and down and told him he'd have to buy extra seat Kim Kardashian takes a VERY dramatic tumble in towering $80 'stripper heels' and accidentally grabs an'old lady' as she falls on her way out of Vanity Fair Oscar party Everything JFK Jr told friends about his love affair with'sexual dynamo' Madonna... her unprintable pillow talk... and his perverse incest request that she couldn't go through with Saudi, UAE and Qatar energy facilities are evacuated after Iran threatens'full scale economic war' as oil price jumps 5%: Live updates New PILL for psoriasis approved... giving hope to millions suffering from debilitating skin condition How I lost 8st in my 50s and now finally have the figure of my dreams. I've been large my whole life, but I now feel happier than I ever did in my 20s. New York City's accent is dying out, study finds It's something that's fixed from roughly the age of 14. But your accent could be hindering your job prospects, according to a new study.

This adorable sea lion has to eat five to eight percent of its body weight every day to stay healthy and hydrated. Breakthroughs, discoveries, and DIY tips sent six days a week. Over the long and complicated course of evolutionary history, mammals independently turned towards water to make a home multiple times. While many of the warm-blooded animals that abandoned dry land for a watery habitat no longer exist, we still have plenty of stunning examples: Think dolphins, whales, manatees, porpoises. There's even a whole suborder of carnivores called the pinnipeds, which includes seals, sea lions, and walruses who move between land and water.

Charles Bennett and Gilles Brassard pioneered quantum information theory. Now they've been awarded the highest honor in computer science. Today it's widely acknowledged that the future of computing will involve the quantum realm . Companies like Google, Microsoft, IBM, and a few well-funded startups are frantically building quantum computers and routinely claiming advances that seem to bring this exotic, world-changing technology within reach. In 1979 all of this was unthinkable.

Importantly, this matches the optimal bound $G\|w_\star\|\sqrt{T}$ available with such knowledge (up to logarithmic factors), unless either $\|w_\star\|$ or $G$ is so large that even $G\|w_\star\|\sqrt{T}$ is roughly linear in $T$. Thus, at a high level it matches the optimal bound in all cases in which one can achieve sublinear regret.

Collecting training data from untrusted sources exposes machine learning services to poisoning adversaries, who maliciously manipulate training data to degrade the model accuracy. When trained on offline datasets, poisoning adversaries have to inject the poisoned data in advance before training, and the order of feeding these poisoned batches into the model is stochastic. In contrast, practical systems are more usually trained/fine-tuned on sequentially captured real-time data, in which case poisoning adversaries could dynamically poison each data batch according to the current model state. In this paper, we focus on the real-time settings and propose a new attacking strategy, which affiliates an accumulative phase with poisoning attacks to secretly (i.e., without affecting accuracy) magnify the destructive effect of a (poisoned) trigger batch. By mimicking online learning and federated learning on MNIST and CIFAR-10, we show that model accuracy significantly drops by a single update step on the trigger batch after the accumulative phase. Our work validates that a well-designed but straightforward attacking strategy can dramatically amplify the poisoning effects, with no need to explore complex techniques.

Recommending out-of-vocabulary (OOV) items is a challenging problem since the in-vocabulary (IV) items have well-trained behavioral embeddings but the OOV items only have content features. Current OOV recommendation models often generate'makeshift' embeddings for OOV items from content features and then jointly recommend with the makeshift' OOV item embeddings and the behavioral IV item embeddings. However, merely using the'makeshift' embedding will result in suboptimal recommendation performance due to the substantial gap between the content feature and the behavioral embeddings. To bridge the gap, we propose a novel User Sequence IMagination (USIM) fine-tuning framework, which first imagines the user sequences and then refines the generated OOV embeddings with the user behavioral embeddings. Specifically, we frame the user sequence imagination as a reinforcement learning problem and develop a recommendation-focused reward function to evaluate to what extent a user can help recommend the OOV items.

Follow-the-Regularized-Leader (FTRL) is a powerful framework for various online learning problems. By designing its regularizer and learning rate to be adaptive to past observations, FTRL is known to work adaptively to various properties of an underlying environment. However, most existing adaptive learning rates are for online learning problems with a minimax regret of $\Theta(\sqrt{T})$ for the number of rounds $T$, and there are only a few studies on adaptive learning rates for problems with a minimax regret of $\Theta(T^{2/3})$, which include several important problems dealing with indirect feedback. To address this limitation, we establish a new adaptive learning rate framework for problems with a minimax regret of $\Theta(T^{2/3})$. Our learning rate is designed by matching the stability, penalty, and bias terms that naturally appear in regret upper bounds for problems with a minimax regret of $\Theta(T^{2/3})$. As applications of this framework, we consider three major problems with a minimax regret of $\Theta(T^{2/3})$: partial monitoring, graph bandits, and multi-armed bandits with paid observations. We show that FTRL with our learning rate and the Tsallis entropy regularizer improves existing Best-of-Both-Worlds (BOBW) regret upper bounds, which achieve simultaneous optimality in the stochastic and adversarial regimes. The resulting learning rate is surprisingly simple compared to the existing learning rates for BOBW algorithms for problems with a minimax regret of $\Theta(T^{2/3})$.

Things to Do in L.A. Tap to enable a layout that focuses on the article. L.A. teachers union widely expected to announce strike date at massive Wednesday rally Members of the largest unions representing teachers and nonteachers participate in joint rally at Grand Park in March 2023. The scene will be repeated on Wednesday, with union members once again on the verge of a strike. This is read by an automated voice. Please report any issues or inconsistencies here .