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Chopra, Anupama (24 July 2005). "Bollywood's Good Girls Learn to Be Bad". The brand new York Times. Tuteja, Joginder (four October 2004). "Aitraaz: Music Review". Adarsh, Taran (20 November 2004). "Top five movies of the week". Adarsh, Taran (18 December 2004). "Mid-Week Top Five". Baddhan, Raj (12 December 2004). "ZEE grabs Bollywood blockbuster Aitraaz". Lakshminarayana, Shruti Indira (7 July 2011). "Two Kannada films will fight it out this weekend". Sharma, Isha (28 June 2015). "7 Bollywood Films That Clashed With SRK And Couldn't Handle It". Adarsh, Taran (11 November 2004). "Aitraaz Review". Kamath, Sudhish (19 November 2004). "Aitraaz Review". Mamtora, Jay. "Aitraaz Review". N, Patcy (12 November 2004). "Aitraaz thrills audiences!". Chopra, Anupama (29 November 2004). "Siren's name". Jha, Subhash K. (15 November 2004). "Aitraaz Review". Pillai, Jitesh (15 November 2004). "Aitraaz". The Times of India. IIFA Awards. 26 June 2005. Star Plus. Zee Cine Awards. 26 March 2005. Zee Tv. Chowdhury, Purna (2010). "Bollywood Babes: Body and Female Desire in the Bombay Films For the reason that Nineties and Darr, Mohra and Aitraaz: A Tropic Discourse". In Mehta, Rini Bhattacharya; Pandharipande, Rajeshwari V. (eds.). Bollywood and Globalization: Indian Popular Cinema, Nation, and Diaspora.

Title:Can LLMs Beat Classical Hyperparameter Optimization Algorithms? View a PDF of the paper titled Can LLMs Beat Classical Hyperparameter Optimization Algorithms? Abstract:The autoresearch repository allows an LLM agent to optimize hyperparameters by enhancing coaching code instantly. We use it as a testbed to check classical HPO algorithms in opposition to LLM-based mostly methods on tuning the hyperparameters of a small language mannequin under a fixed compute price range. When defining a fixed search space over autoresearch, classical methods similar to CMA-ES and TPE persistently outperform LLM-primarily based brokers, where avoiding out-of-reminiscence failures matters more than search range. Allowing the LLM to straight edit source code narrows the hole to the classical strategies but doesn't shut it, even with frontier models obtainable on the time of writing corresponding to Claude Opus 4.6 and Gemini 3.1 Pro Preview. We observe that LLMs wrestle to track optimization state throughout trials. In contrast, classical strategies lack the area knowledge of LLMs. To combine the strengths of both, we introduce Centaur, a hybrid that shares CMA-ES's interpretable internal state, including imply vector, step-measurement, and covariance matrix, with an LLM. Centaur achieves the best lead to our experiments, and a 0.8B LLM already suffices to outperform all classical and pure LLM strategies. Unconstrained code enhancing requires larger fashions to be competitive with classical strategies. We additional analyze search diversity, mannequin scaling from 0.8B to frontier models, and ablate the fraction of LLM-proposed trials in Centaur. All in all, our results suggest that LLMs are simplest as a complement to classical optimizers, not as a alternative. Code is available at this https URL & interactive demo at this https URL.

Abstract:Asynchronous reinforcement learning can improve language-mannequin submit-training throughput by decoupling response generation from policy optimization, but stale responses introduce distribution drift. Standard habits-corrected methods management this drift with behavior-coverage probabilities, significance ratios, or clipping, which requires token-aligned, versioned, and numerically constant conduct log-probabilities throughout rollout and learner methods. We ask whether asynchronous group-relative RL can as an alternative be stabilized utilizing only current-policy probabilities. We establish a scale-imbalance failure mode: when stale responses are evaluated underneath the current policy, positive and adverse loss phrases can appear at totally different negative log-chance scales, so zero-sum benefits now not imply balanced loss contributions. We propose Asymmetric-Scale Policy Optimization (ASymPO), which normalizes every response's token loss by its current average token unfavorable log-chance. ASymPO requires no habits-coverage probabilities, restores response-degree zero-sum balance, and preserves a nonzero studying signal. We also introduce Scaled Policy Optimization (SPO), a hard and fast destructive-scaling baseline, and evaluate both present-coverage-solely objectives in asynchronous mathematical reasoning put up-coaching.

The Arch Deluxe was first examined as a "Taste of the Month" burger in October 1995 at McDonald's eating places in Canada. Afterwards, the Arch Deluxe was officially launched in May 1996 in probably the most expensive advertising campaigns thus far. The model was nonetheless offered at choose restaurants throughout 1998 and 1999. On August 18, 2000, the Arch Deluxe was lastly discontinued, and is now not discovered at McDonald's stores. McDonald's is estimated to have spent over US$300 million (equal Agreement to Protect Property During a Split-Up $616 million in 2025) on the research, production, and advertising and marketing for the Arch Deluxe. The company acknowledged in 2003 that some of its initial research into adult marketing was reused in the event of its profitable line of salads. Cronin, Jeff (May 3, 1996). "McDonald's Targets Americans' Hearts". United States Food and Drug Administration (2024). "Daily Value on the Nutrition and Supplement Facts Labels". Collins, Glenn (19 September 1996). "Chief of McDonald's Defends Arch Deluxe to Franchisees". The new York Times. Taylor, Kate (January 3, 2018). "McDonald's is bringing again considered one of its most costly failures - with one major distinction". McGrath, Jane. "5 Failed McDonald's Menu Items". Glass, Jeremy (2021-07-23). "The Arch Deluxe Was a Hell of a Burger. It Was Also McDonald's Most Expensive Flop". Grant, Charley (November 15, 2019). "There Isn't Enough Special Sauce to Win the Burger Wars". Bock, Wally (March 17, 2003). "McDonald's: When the passion is Gone, the Profits are Over". Sid & Marty Krofft Television Productions Inc. v. McDonald's Corp.