How Do People Churn Out RSI Papers? An Unconventional Survey We’ve published an unconventional survey of RSI that tackles a much-discussed question head-on: there seem to be quite a few “filler” papers in RSI these days. Where do people get their ideas for them, and how can we follow their lead to churn out papers of our own? We offer two approaches: combining different design dimensions, and borrowing from traditional deep learning. First, we noticed that many RSI methods share similar underlying ideas, differing mainly in design choices such as what to change, how often to change it, and what feedback to use. We therefore developed a nine-dimensional taxonomy to systematically capture the commonalities and differences across method designs. All methods can be viewed as combinations of choices across these dimensions. From there, finding a new combination means finding a new idea. The other approach is to rummage through the deep learning toolbox and see what can be brought over to RSI. Some RSI methods are already borrowing from deep learning: SkillAdam, for example, draws on the Adam optimizer to guide skill revision, while RRSI uses regularization to constrain the evolution of agent code. Familiar tools such as optimizers, regularization, and curriculum learning could all become the starting point for a new paper when transplanted into RSI. We’ve compiled the relevant papers and their classifications on the Awesome RSI website, where you can browse and filter them by design dimension. Come find some inspiration, and feel free to submit a PR to the GitHub repository to add great RSI papers! arXiv: arxiv.org/abs/2610.14625 Website: prism-shadow.github.io/aweso… GitHub: github.com/Prism-Shadow/awes… Zhihu: zhuanlan.zhihu.com/p/2093754… Finally, we wish you all enough “filler” papers to graduate soon, so you can go do what you truly love. Disclaimer Expressions used in this post, including “filler,” “filler papers,” “churning out papers,” and “following their lead,” are informal, self-deprecating academic banter. They do not constitute a quality assessment of any specific paper, author, research group, institution, conference, journal, reviewer, research area, or any affiliated WeChat group. We are not saying that all RSI papers are filler, nor that RSI is a field of filler papers, nor that everyone working on RSI is just churning them out. In fact, the authors of this post are also working on RSI and have just published an RSI survey themselves, so any interpretation that somehow exempts us entirely from this criticism would be an overly charitable misreading. We acknowledge that RSI is an important, promising research direction that deserves serious study… (another 100 million tokens omitted).