China’s ByteDance discovers new scaling law that could sustain AI boom

ByteDance's Seed AI team discovered a new scaling law showing AI agents can double learning speed every three months through real-world interaction, potentially sustaining the AI boom as traditional training methods face data shortages. The finding, published in a research paper, introduces EdgeBench, a benchmarking suite with 134 long-horizon tasks to study agent learning after deployment.

China’s ByteDance discovers new scaling law that could sustain AI boom Researchers at the Chinese tech giant have been analysing how fast AI agents can improve by performing real-world tasks scaling law https://www.scmp.com/news/china/science/article/3292417/did-chinas-baidu-discover-scaling-laws-openai-debate-rekindles-ai-circles?module=inline&pgtype=article governing how fast artificial intelligence agents can improve by performing real-world tasks, a finding that could help prolong the AI boom just as traditional development methods hit a wall. In a research paper published on Thursday, ByteDance’s Seed AI team revealed that AI agents – autonomous software that executes tasks on a human’s behalf – can double their learning speed every three months by interacting with real-world environments over extended periods. The finding comes as the global AI industry searches for new ways to improve models. For years, developers relied on feeding systems more data and computing power during initial training, but prominent industry figures – including OpenAI co-founder Andrej Karpathy – have warned that this brute-force approach cannot last forever. a looming data drought https://www.scmp.com/tech/policy/article/3356498/global-ai-data-shortage-looming-china-boosts-its-own-supply?module=inline&pgtype=article . US-based research institute Epoch AI recently warned that publicly available, human-generated text data could be depleted within the next six years. That makes finding alternative paths to advance AI one of the industry’s highest priorities. However, despite the fact that tech firms are pivoting towards agentic AI, ByteDance researchers noted in the paper that how these autonomous systems “learn from real-world environments after deployment remains far less understood”. To address the problem, the team developed EdgeBench, a benchmarking suite featuring 134 ultra-long-horizon tasks spanning a wide range of areas from software engineering and scientific discovery to formal mathematics and professional knowledge work. Each task requires at least 12 hours of continuous AI agent operation.