Recursive self-improvement (RSI) enables large language models to advance their capabilities through successive updates, but these updates can also introduce new risks and weaken existing safeguards. In line with the AI-45{\deg} Law, we pursue Safety RSI, in which safety develops as an intrinsic capability alongside ge...
Jing-Nan Zheng, Cheng Jin, Ming Zhang et al.· 0 citations
Recursive self-improvement, the participation of AI systems in improving their own capabilities, is beginning to move from theoretical prospect to practice, posing both challenges and opportunities for safety alignment. Models evolve through frequent updates, and their safety alignment requires continual adaptation to...
Jing-Nan Zheng, Dong-Cheng Zhang, Yi Zhang et al.· 1 citation
Fetch-then-Explore is proposed, which separates page selection from evidence extraction and keeps what it selects: pages are recorded in a per-question workspace on the filesystem rather than the context window or a transient session, and evidence is pulled from them on demand later.
Qi Liu, Yi-Qun Chen, Zidan Chen et al.· 0 citations
This work proposes \the authors', a novel method designed to instill and refine proactive exploration in LLM agents, which consists of two components: Exploratory Data Construction, which synthesizes exploration-rich trajectories to mitigate the hindsight bias of standard demonstrations; and RL Optimization with Contra...
Zhizhao Guan, Chen Huang, Zi-Ming Liu et al.· 1 citation
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
