OdysSim: Building Foundation Models for Human Behavior Simulation

Abstract

Large language models are increasingly used as human simulators for interactive evaluation and social simulation, yet current post-training often produces homogeneous, overly helpful systems unlike real people, creating a behavioral Sim2Real gap that prompting alone cannot close. We present Odyssim, a large-scale open study of behavioral foundation models organized around SOUL, whose five capability Axes (CONV, SS, COG, ROLE, EVAL) jointly guide data curation and evaluation. We release three artifacts: (1) SOULIndex, a 23-task evaluation suite for human-behavior simulation; (2) OdysSim, a corpus of 21.4M interactions, approximately 10B tokens, from 63 public sources, unified into a conversational format and retrofitted with back-generated social contexts; and (3) an end-to-end recipe combining midtraining, task-specific RL with VFRL, and expert distillation into a single deployable model. On SOULIndex, OdysSim-8B achieves an average score of 62.4, comparable to frontier models such as GPT-5.5 (62.6), ranks first on 8 of 23 tasks, and outperforms prior open behavioral-simulation baselines by 14.2 points on average. We also identify and mitigate reward-hacking failures in judge-based behavioral RL, including judge manipulation and short-response collapse. We release the corpus, benchmark, recipes, and checkpoints.