Scaling in Games: Continued Pre-Training For Embodied Agents In Diverse Virtual Worlds

Key points

• Whether these gains extend to virtual worlds with different visual styles, dynamics, controls, and objectives remains unclear.1
• We investigate this question through a controlled study of continued pre-training (CPT) across three axes: data volume, diversity, and model size.1
• We assess performance and generalization using three-level validation losses and gameplay benchmarks from in-corpus worlds to unseen worlds.1
• However, the limits of imitation and persistent gaps in distant worlds suggest that building generalist embodied agents requires richer training stages beyond continued pre-training.1

Sources (2)

• [1]Scaling in Games: Continued Pre-Training For Embodied Agents In Diverse Virtual Worlds
  Hugging Face Daily Papers · Oct 20, 12:00 AM

  Scaling data, diversity and model capacity has improved embodied learning in physical environments, where diverse experiences remain grounded in shared physical laws.

  Whether these gains extend to virtual worlds with different visual styles, dynamics, controls, and objectives remains unclear.

• [2]Scaling in Games: Continued Pre-Training For Embodied Agents In Diverse Virtual Worlds
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 20, 11:09 AM · same content

Extractive summary: sentences quoted from the sources.