nan zjn20030811

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Building reliable learning systems across perception, generation, and control.

🤖 VLA & Robotics
_{PI0/PI0.5, flow matching, policy learning, simulation, and control}

🌍 World Models
_{Video prediction, planning rollouts, guidance, and model-based RL}

⚡ Efficient AI
_{Diffusion, quantization, distributed training, and reliable LLM systems}

Core algorithm, training, inference, and evaluation improvements merged into upstream projects.

VLA & WAM FluxVLA · PI0/PI0.5 horizon and attention alignment tau-0-vla · Beta flow-matching time sampling WAM-Flow · variable-length text training path BiWM · Wan DMD guidance anchor pi-zero-pytorch · batched RTC guidance weights	World models & control nano-world-model · multi-frame planning rollout alignment stable-worldmodel · validation-safe TD-MPC2 scaling Psi0 · distributed token-mixture synchronization Gymnasium · deterministic mutable-observation snapshots pypose · autograd-safe quaternion normalization
Diffusion & generative modeling LightX2V · symmetric FP8/INT8 Triton quantization denoising-diffusion-pytorch · Forward XM guidance compatibility vit-pytorch · low-precision-stable DINO loss vector-quantize-pytorch · masked residual beam loss	LLM & inference systems x-transformers · beam-search KV-cache reordering 15 merged projects spanning training, inference, and evaluation systems.

View the complete merged-contribution list

Area	Project	Merged contribution
VLA	FluxVLA · PR #108	Runtime PI0/PI0.5 action-horizon and attention-shape alignment
VLA / flow matching	tau-0-vla · PR #13	Correct Beta time sampling and device-safe flow statistics
WAM / diffusion	WAM-Flow · PR #20	Variable-length text training and unquantized stage-2 path
World model	BiWM · PR #13	Wan DMD guidance-anchor correction
World model	nano-world-model · PR #20	Multi-frame planning-rollout alignment
World model / RL	stable-worldmodel · PR #322	TD-MPC2 running Q-value scaling frozen during validation
Diffusion / quantization	LightX2V · PR #1474	Symmetric round-to-nearest FP8/INT8 Triton quantization
Diffusion	denoising-diffusion-pytorch · PR #369	Classifier-free guidance compatibility for Forward XM
Diffusion	vit-pytorch · PR #372	Numerically stable DINO distillation in low precision
Generative modeling	vector-quantize-pytorch · PR #254	Masked residual beam-loss aggregation for batched sequences
VLA / policy	pi-zero-pytorch · PR #27	Batched RTC guidance-weight handling
Transformers	x-transformers · PR #357	Self-attention KV-cache reordering after beam pruning
Robotics / geometry	pypose · PR #408	Out-of-place Lie-group quaternion normalization with autograd
RL / environments	Gymnasium · PR #1682	Deterministic snapshots for reused mutable observations
Distributed training	Psi0 · PR #103	Token-mixture batch synchronization across ranks

Correctness first — make edge cases explicit and testable.
Reproducibility — keep randomness, distributed behavior, and evaluation stable.
Efficiency — use memory-, precision-, and throughput-aware implementations.

_{Open-source contributions are linked to their upstream pull requests.}