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Mean field games (MFG) are a class of mathematical models that study strategic decision-making by a large number of agents. Here, the collection of agents is also referred to as the crowd. Mean field games with optimal stopping are a type of mean field game where the agents in the game must choose when to stop playing.

Sections E.1 to E.3 detail the accuracies and instructions found through prompt optimization using PaLM 2-L-IT and GPT-3.5-turbo on BBH tasks. The results reveal that both optimizers yield improvements over baseline instructions, with notable differences in instruction styles depending on the starting point of the optimization process.

In this section, we introduce the PDE system for the mixed strategy equilibrium of mean field games with optimal stopping (OSMFG) We first present the single agent optimal stopping problem. We then proceed to introduce the OSMFG framework. We note that in some regions of {u = ψ}, agents might be indifferent between leaving the game and remaining in the game.

Figures 23 and 24 illustrate prompt optimization curves for 21 BBH tasks, showcasing consistent upward trends when utilizing the text-bison scorer and PaLM 2-L-IT optimizer. This indicates effective optimization across multiple tasks.

A visual breakdown of Paul Graham's "How to Do Great Work" essay. Part 1 covers finding your field, making progress, and cultivating habits for great work. Key takeaways: choose a field you're naturally good at and ambitious about, invest consistently in small steps, balance active and passive thinking, aim high, be original, and prioritize quality in every aspect of your work.

This section discusses the varying styles of meta-prompts that yield the best results for different optimizer models. It highlights examples for PaLM 2-L and GPT models, detailing how generated instructions are structured to optimize the output of scorer LLMs on the GSM8K dataset.

The theory of mean field games (MFGs) provides a framework for modeling games with a large number of players. In an MFG model, each player controls their own trajectory based on the expected distribution of the states of all players. This paper proposes a generalized fictitious play algorithm that computes OSMFG mixed equilibria.

Cloris Chen, CEO of Cogito Finance, discusses the convergence of traditional finance and DeFi through asset tokenization, AI-driven portfolio management, and the challenges of scaling RWA products in the evolving financial landscape.

This section introduces the meta-prompt specifically crafted for math optimization, focusing on its role in guiding large language models (LLMs) to generate enhanced solutions for various mathematical problems. The strategic design of the meta-prompt is emphasized for optimizing performance.

This section discusses limitations of large language models (LLMs) in optimization tasks, including their tendency to hallucinate mathematical values, failure to generate new solutions despite explicit instructions, and challenges in reaching optimal points in black-box optimization. Potential solutions include implementing mechanisms to trigger external tool usage, improving instruction-following consistency, and enhancing exploration during optimization to avoid stagnation at suboptimal points.

This section illustrates the use of prompting formats for scorer large language models (LLMs), focusing on examples of Q_begin, Q_end, and A_begin formats when the "QA" pattern is present. It emphasizes that the "QA" pattern is eliminated in instruction-tuned scorer models like text-bison when employing the Q_begin and Q_end formats.

SINGAPORE - 9:00PM, September 24, 2024 — The Open Art, hosted by Blum, TONX, and TON Society and co-hosted by CoinGecko, All At Once, TonBit, and Google Cloud, attracted over 11,280 registered attendees—the highest number of registered attendees during Token2049 Week in Singapore. Held on September 19 at the Nationa...

In this conclusion, we summarize the successful use of LLMs as optimizers in prompt optimization, showing that optimized prompts significantly outperform human-designed counterparts. While LLMs demonstrated effectiveness, challenges remain, particularly in balancing exploitation and exploration and utilizing error cases for generating improved instructions. Future research will focus on enhancing feedback mechanisms and optimizing prompt generation to reduce sensitivity to initialization and minimize training set...

How are you, hacker? 🪐Want to know what's trending right now?: The Techbeat by HackerNoon has got you covered with fresh content from our trending stories of the day! Set email preference here. Data Migration from Dell ECS to MinIO By @minio [ 7 Min read ] Dell ECS clusters allow you to migrate your data to any S3 c...

This article will guide you through creating a flexible and dynamic content platform using WordPress as a headless CMS, Custom Post Types (CPTs), and Advanced Custom Fields (ACF) Whether you’re a seasoned developer or just starting out, this combination offers a powerful foundation for your projects.

The goal is to set up an interface where you can easily view and edit data. Imagine a spreadsheet displaying products, prices, and quantities. By using React's state management, you can dynamically manage the spreadsheet's information. This keeps the application flexible and ensures that the information is always in sync.

Waiting until November to plan for Black Friday and Cyber Monday? That’s like stepping into the ring against a heavyweight champ without any training. In the ultra-competitive arena of e-commerce, the early planners grab the lion’s share of profits. Here’s how you can dominate this year’s biggest shopping days, with...

This week, HackerNoon spotlights Wellfound, a platform connecting startups and job seekers with a focus on transparency. Wellfound has teamed up with HackerNoon for Targeted Ads and is now expanding its sponsorship to include Startups of the Year!

This section summarizes various approaches in prompt optimization, including soft prompt-tuning, discrete optimization via gradient-guided search, and edit-based techniques. It discusses the use of language models in generating and refining prompts, compares methods like APE and APO, and highlights the integration of natural language feedback for improving LLM performance. The work also notes the role of language models as mutation and crossover operators in...

This section discusses overfitting in prompt optimization, noting that while training accuracies may exceed test accuracies, they can still yield effective results if solutions overfit similarly. We compare our OPRO approach with EvoPrompt's Genetic Algorithm and Differential Evolution methods, revealing that OPRO's use of optimization trajectories and exemplars leads to more stable and effective prompt performance across benchmarks like GSM8K and BBH sports understanding.

This article presents findings from our ablation studies using text-bison and PaLM 2-L to optimize prompt performance. Key insights include the impact of instruction order, the importance of exemplars, and the effects of initial instructions on optimization stability and accuracy in math and sports understanding tasks. Our results indicate that carefully crafted meta-prompts significantly enhance model performance.

This section discusses prompt optimization results on GSM8K and BBH tasks, showing significant improvements in training accuracy through various instructions. It emphasizes the importance of nuanced instruction variations and the transferability of optimized prompts across different math reasoning benchmarks.

OPRO enhances LLM performance in tasks like GSM8K and BBH by optimizing instructions using models like PaLM 2-L and GPT-4. Experiments reveal improved transferability of optimized instructions across mathematical reasoning datasets.