AI Dataset2026
Evaluates whether models can reconstruct executable 3D worlds from visual or textual inputs by providing Blender-based tasks, assets, and automated evaluation for code-driven realizations.
Improves token-level credit assignment for RL fine-tuning of language models by parameterizing the critic as cumulative, position-wise log-odds increments (Tracking-Value PPO), enabling more accurate value-evolution tracking under sparse terminal rewards and boosting math-reasoning and coding benchmarks.