Meta Platforms(US:META) 机器之心·2026-01-02 03:12Core Viewpoint - Meta is pursuing the ambitious goal of developing "superintelligent" AI, which aims to create autonomous AI systems that surpass human expert levels. This initiative has faced skepticism from experts like Yann LeCun, who believes the path to superintelligence is impractical [1]. Group 1: SSR Methodology - The Self-play SWE-RL (SSR) method is introduced as a new approach to training superintelligent software agents, which can learn and improve without relying on existing problem descriptions or human supervision [2][4]. - SSR leverages self-play systems, similar to AlphaGo, allowing software agents to interact with real code repositories to autonomously generate learning experiences [2][4]. - The SSR framework operates with minimal reliance on human data, assuming access to sandboxed code repositories with source code and dependencies, eliminating the need for manually annotated issues or test cases [4]. Group 2: Bug Injection and Repair Process - The SSR framework involves two roles: a bug-injection agent that introduces bugs into a codebase and a bug-solving agent that generates patches to fix these bugs [8][9]. - The bug-injection agent creates artifacts that intentionally introduce bugs, which are then verified for consistency to ensure they are reproducible [9][11]. - The bug-solving agent generates final patches based on the defined bugs, with success determined by the results of tests associated with those bugs [11][12]. Group 3: Performance Evaluation - Experimental results show that SSR demonstrates stable and continuous self-improvement even without task-related training data, indicating that large language models can enhance their software engineering capabilities through interaction with original code repositories [17]. - SSR outperforms traditional baseline reinforcement learning methods in two benchmark tests, achieving improvements of +10.4% and +7.8% respectively, highlighting the effectiveness of self-generated learning tasks over manually constructed data [17]. - Ablation studies indicate that the self-play mechanism is crucial for performance, as it continuously generates dynamic task distributions that enrich the training signals [19][20]. Group 4: Implications for AI Development - SSR represents a significant step towards developing autonomous AI systems that can learn and improve without direct human supervision, addressing fundamental scalability limitations in current AI development [21][22]. - The ability of large language models to generate meaningful learning experiences from real-world software repositories opens new possibilities for AI training beyond human-curated datasets, potentially leading to more diverse and challenging training scenarios [22]. - As AI systems become more capable, the ability to learn autonomously from real-world environments is essential for developing intelligent agents that can effectively solve complex problems [25].