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Looking for arXiv endorsement (cs.LG) – RL fine-tuning for VLMs (GRPO, MathVista)
Hi everyone, I am seeking an arXiv endorsement for cs.LG (Machine Learning) to submit my first paper on RL fine-tuning for vision-language models. Background: MS in AI (Purdue), working on RL + VLM training systems. Paper: A Case Study of Staged Metric-Gated GRPO for Visual Numeric Reasoning PDF: https://github.com/kgaero/RL_GSPO_Qwen2.5VLM/blob/main/paper/staged_metric_gated_grpo.pdf Short summary: Staged RL fine-tuning pipeline for VLMs (GRPO-based) Curriculum over MathVista subsets Metric-gated reward adaptation (structure → correctness) Checkpoint-aware continuation via alias-based selection Main result: Exact-match improves 0.375 → 0.75 with stable structure under constrained compute. If you’re eligible to endorse (cs.LG or related), I’d greatly appreciate it. Happy to share endorseme
Ask HN: Learning resources for building AI agents?
I’ve recently gone through several materials, including Antonio Gulli’s AI Agentic Design Patterns, Sam Bhagwat’s Principles of Building AI Agents and Patterns for Building AI Agents, as well as the courses from LangGraph Academy and some content on DataCamp. This space is evolving very quickly, so I’m curious how others here are approaching learning. What resources, courses, papers, or hands-on approaches have you found most useful while building AI agents? Comments URL: https://news.ycombinator.com/item?id=47637083 Points: 2 # Comments: 3
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Multi-fidelity approaches for general constrained Bayesian optimization with application to aircraft design
Aircraft design relies heavily on solving challenging and computationally expensive Multidisciplinary Design Optimization problems. In this context, there has been growing interest in multi-fidelity models for Bayesian optimization to improve the MDO process by balancing computational cost and accuracy through the combination of high- and low-fidelity simulation models, enabling efficient exploration of the design process at a minimal computational effort. In the existing literature, fidelity selection focuses only on the objective function to decide how to integrate multiple fidelity levels, — Oihan Cordelier, Youssef Diouane, Nathalie Bartoli
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