Bashlykov, Nikolay
MLGym: A New Framework and Benchmark for Advancing AI Research Agents
Nathani, Deepak, Madaan, Lovish, Roberts, Nicholas, Bashlykov, Nikolay, Menon, Ajay, Moens, Vincent, Budhiraja, Amar, Magka, Despoina, Vorotilov, Vladislav, Chaurasia, Gaurav, Hupkes, Dieuwke, Cabral, Ricardo Silveira, Shavrina, Tatiana, Foerster, Jakob, Bachrach, Yoram, Wang, William Yang, Raileanu, Roberta
We introduce Meta MLGym and MLGym-Bench, a new framework and benchmark for evaluating and developing LLM agents on AI research tasks. This is the first Gym environment for machine learning (ML) tasks, enabling research on reinforcement learning (RL) algorithms for training such agents. MLGym-bench consists of 13 diverse and open-ended AI research tasks from diverse domains such as computer vision, natural language processing, reinforcement learning, and game theory. Solving these tasks requires real-world AI research skills such as generating new ideas and hypotheses, creating and processing data, implementing ML methods, training models, running experiments, analyzing the results, and iterating through this process to improve on a given task. We evaluate a number of frontier large language models (LLMs) on our benchmarks such as Claude-3.5-Sonnet, Llama-3.1 405B, GPT-4o, o1-preview, and Gemini-1.5 Pro. Our MLGym framework makes it easy to add new tasks, integrate and evaluate models or agents, generate synthetic data at scale, as well as develop new learning algorithms for training agents on AI research tasks. We find that current frontier models can improve on the given baselines, usually by finding better hyperparameters, but do not generate novel hypotheses, algorithms, architectures, or substantial improvements. We open-source our framework and benchmark to facilitate future research in advancing the AI research capabilities of LLM agents.
Llama 2: Open Foundation and Fine-Tuned Chat Models
Touvron, Hugo, Martin, Louis, Stone, Kevin, Albert, Peter, Almahairi, Amjad, Babaei, Yasmine, Bashlykov, Nikolay, Batra, Soumya, Bhargava, Prajjwal, Bhosale, Shruti, Bikel, Dan, Blecher, Lukas, Ferrer, Cristian Canton, Chen, Moya, Cucurull, Guillem, Esiobu, David, Fernandes, Jude, Fu, Jeremy, Fu, Wenyin, Fuller, Brian, Gao, Cynthia, Goswami, Vedanuj, Goyal, Naman, Hartshorn, Anthony, Hosseini, Saghar, Hou, Rui, Inan, Hakan, Kardas, Marcin, Kerkez, Viktor, Khabsa, Madian, Kloumann, Isabel, Korenev, Artem, Koura, Punit Singh, Lachaux, Marie-Anne, Lavril, Thibaut, Lee, Jenya, Liskovich, Diana, Lu, Yinghai, Mao, Yuning, Martinet, Xavier, Mihaylov, Todor, Mishra, Pushkar, Molybog, Igor, Nie, Yixin, Poulton, Andrew, Reizenstein, Jeremy, Rungta, Rashi, Saladi, Kalyan, Schelten, Alan, Silva, Ruan, Smith, Eric Michael, Subramanian, Ranjan, Tan, Xiaoqing Ellen, Tang, Binh, Taylor, Ross, Williams, Adina, Kuan, Jian Xiang, Xu, Puxin, Yan, Zheng, Zarov, Iliyan, Zhang, Yuchen, Fan, Angela, Kambadur, Melanie, Narang, Sharan, Rodriguez, Aurelien, Stojnic, Robert, Edunov, Sergey, Scialom, Thomas
In this work, we develop and release Llama 2, a collection of pretrained and fine-tuned large language models (LLMs) ranging in scale from 7 billion to 70 billion parameters. Our fine-tuned LLMs, called Llama 2-Chat, are optimized for dialogue use cases. Our models outperform open-source chat models on most benchmarks we tested, and based on our human evaluations for helpfulness and safety, may be a suitable substitute for closed-source models. We provide a detailed description of our approach to fine-tuning and safety improvements of Llama 2-Chat in order to enable the community to build on our work and contribute to the responsible development of LLMs.