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25a8f5d verified 4 months ago

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library_name: pytorch
tags:
  - world-model
  - diffusion
  - dit
  - meanflow
  - counter-strike
  - opencs2

CS2 World Model — action-conditioned latent DiT (OpenCS2)

Action-conditioned latent world model for OpenCS2: given a few recent frames and the player's actions, it predicts the next frame. It never touches pixels — history, target and prediction are all 32×8×8 latents produced by the frozen CS2 RAE tokenizer. Code: github.com/julien-blanchon/world-model.

Dynamics core: ~205M-param in-context DiT (hidden=1024, depth=16, heads=16, register_tokens=4, always-on within-frame conv-stem).
Transport: iMeanFlow with plain-Gaussian noise (no OT coupling), schedule-shift α=2, plain MSE. A frame samples in one network evaluation (NFE=1).
Conditioning: history_frames=4, sequence_frame_gap=1, action-conditioned.
Training: AdamW lr 2e-4, warmup 2000, EMA 0.9995, DIAMOND scheduled sampling with an nas depth curriculum (1 → 2 → 4) and a context-noise ramp (0.05 → 0.2).

Files

File	Description
`world-model.pt`	Final world-model checkpoint (`runs/world_model/world-model.pt`).

Usage

Requires the matching CS2 RAE tokenizer (d32) and the world-model codebase.

Internal research checkpoint. Provided as-is, no warranty.