iclr

Fair Reinforcement Learning introduces Democratic Alignment to incorporate multiple competing value sets from different agents, overcoming traditional RLHF limitations, and achieves orders of magnitude faster optimization via a black-box policy wrapper.

This article presents a dataset and analysis pipeline for ICLR 2026 accepted papers, extracting institutional affiliations from PDF title blocks to create a clean dataset and publication-ready treemap visualizations.

MIT CSAIL researchers introduce RLCR, a method using Brier scores in reinforcement learning to train AI models to output calibrated confidence estimates, significantly reducing overconfidence without sacrificing accuracy.

A PhD student at ICLR seeks practical tactics to overcome social anxiety and break into existing conversation groups without generic confidence advice.

Two ICLR 2026 papers show how small RL-trained agents outperform frontier models on machine-learning engineering tasks and how MLE-Smith automatically scales MLE workloads.

AutoFigure is an open-source system for generating and refining editable, publication-ready scientific diagrams, accepted to ICLR 2026.