Using RL for improving the performance of systems has a lot of potential, but is also in many ways very different from, for example, using RL for games. Thus, in this work we first discuss the unique challenges RL for systems has, and then propose Park an open extensible platform, which makes it easier for ML researchers to work on systems problems. Currently, Park consists of 12 real world system-centric optimization problems with one common easy to use interface. Finally, we present the performance of existing RL approaches over those 12 problems and outline potential areas of future work.

Today's LLM ecosystem comprises a wide spectrum of models that differ in size, capability, and cost. No single model is optimal for all scenarios; hence, LLM routers have become essential for selecting the most appropriate model under varying circumstances. However, the rapid emergence of various routers makes choosing the right one increasingly challenging. To address this problem, we need a comprehensive router comparison and a standardized leaderboard, similar to those available for models. In this work, we introduce RouterArena, the first open platform enabling comprehensive comparison of LLM routers. RouterArena has (1) a principally constructed dataset with broad knowledge domain coverage, (2) distinguishable difficulty levels for each domain, (3) an extensive list of evaluation metrics, and (4) an automated framework for leaderboard updates. Leveraging our framework, we have produced the initial leaderboard with detailed metrics comparison as shown in Figure 1. Our framework for evaluating new routers is on https://github.com/RouteWorks/RouterArena. Our leaderboard is on https://routeworks.github.io/.

It is great to see the kind of interest in applying machine learning, and specifically reinforcement learning, into real-world problems such as computer systems as presented in this paper. While the paper has no significant contributions on either a theoretical or algorithmic front, it does an important job at highlighting some of the issues in applying modern RL algorithms to real problems, and provides a necessary benchmarking environment for computer systems research specifically. The problem domains included have a wide variety of characteristics, from high-frequent real-time systems to very-long horizon problems, uniquely structured state and action spaces and both simulated and real environments (some other related work that could be added is [1]). Especially the latter is valuable to ground any research. Moreover, the authors provide an RL baseline result for each of the proposed tasks, and highlight some of the problematic characteristics of these tasks for RL specifically. There could be a more elaborate discussion of the results however.

The reviewers have each reviewed this paper carefully, and have taken the author response into account. There is clear consensus among them that this paper is a valuable contribution to the research community, both in helping to bring the application area of ML for systems environment more into the conversation and for providing a solid suite of benchmarks to foster further innovation within the community. I especially appreciate this aspect of helping to make the future research community more effective. In the author response, the authors describe several ways in which their paper will be revised to take reviewer feedback into account, and I expect this will be done for any final version of the paper.

Using RL for improving the performance of systems has a lot of potential, but is also in many ways very different from, for example, using RL for games. Thus, in this work we first discuss the unique challenges RL for systems has, and then propose Park an open extensible platform, which makes it easier for ML researchers to work on systems problems. Currently, Park consists of 12 real world system-centric optimization problems with one common easy to use interface. Finally, we present the performance of existing RL approaches over those 12 problems and outline potential areas of future work.

Large Language Models (LLMs) have unlocked new capabilities and applications; however, evaluating the alignment with human preferences still poses significant challenges. To address this issue, we introduce Chatbot Arena, an open platform for evaluating LLMs based on human preferences. Our methodology employs a pairwise comparison approach and leverages input from a diverse user base through crowdsourcing. The platform has been operational for several months, amassing over 240K votes. This paper describes the platform, analyzes the data we have collected so far, and explains the tried-and-true statistical methods we are using for efficient and accurate evaluation and ranking of models. We confirm that the crowdsourced questions are sufficiently diverse and discriminating and that the crowdsourced human votes are in good agreement with those of expert raters. These analyses collectively establish a robust foundation for the credibility of Chatbot Arena. Because of its unique value and openness, Chatbot Arena has emerged as one of the most referenced LLM leaderboards, widely cited by leading LLM developers and companies. Our demo is publicly available at \url{https://chat.lmsys.org}.

Israel's traffic congestion ranks near the worst among developed economies, but an algorithm can help, says one of the country's IT firms engaged in the auto and mobility sector. ITC, or Intelligent Traffic Control, was one of the artificial intelligence players at Tel Aviv's recent EcoMotion showcase where high-tech and AI firms hope to make transport more efficient and cleaner. Its AI software collects real-time data from road cameras and then sends instructions to manipulate traffic lights based on vehicle flows. "ITC managed to prove mathematically that many traffic jams can be prevented –- if you intervene early enough," said its co-founder and chief technology officer Dvir Kenig, citing a 30 percent drop in traffic at the two junctions using their system. The company says road congestion is a global scourge, calculating that the average driver spends three days a year stuck in traffic, also pumping out greenhouse gas emissions.

Israel's traffic congestion ranks near the worst among developed economies but an algorithm can help, says one of the country's IT firms engaged in the auto and mobility sector. ITC, or Intelligent Traffic Control, was one of the artificial intelligence players at Tel Aviv's recent EcoMotion showcase where high-tech and AI firms hope to make transport more efficient and cleaner. Its AI software collects real-time data from road cameras and then sends instructions to manipulate traffic lights based on vehicle flows. "ITC managed to prove mathematically that many traffic jams can be prevented -- if you intervene early enough," said its co-founder and chief technology officer Dvir Kenig, citing a 30 percent drop in traffic at the two junctions using their system. The company says road congestion is a global scourge, calculating that the average driver spends three days a year stuck in traffic, also pumping out greenhouse gas emissions.

Chemistry laboratories generate a significant amount of data. However, some of it is still in paper format and is difficult to access in its entirety. Three scientists from EPFL present a modular open science platform to manage the large amounts of data produced in chemistry research. Their study entitled " Making the collective knowledge of chemistry open and machine-readable" waspublished in Nature Chemistry. Managing data in modern chemistry is challenging.

In recent years, the advent of deep learning-based techniques and the significant reduction in the cost of computation resulted in the feasibility of creating realistic videos of human faces, commonly known as DeepFakes. The availability of open-source tools to create DeepFakes poses as a threat to the trustworthiness of the online media. In this work, we develop an open-source online platform, known as DeepFake-o-meter, that integrates state-of-the-art DeepFake detection methods and provide a convenient interface for the users. We describe the design and function of DeepFake-o-meter in this work.