Thoughts on generative models, video quality, and the intersection of perception and computation.
A short field note on post-training for video generation in mid-2026: the pretraining recipe that converged, the physics and persistence failures scale is not fixing, the reward models and GRPO variants industry already runs, the three constraints that make video RL genuinely hard, and why it is still the right bet.
Read more →Best-of-N search pays full price for every candidate it throws away. This post shows that the candidate rankings search depends on survive aggressive training-free caching, so you can explore every candidate cheaply and re-generate only the winner at full compute: 94.7% of best-of-8's gain at 63% of its cost, with any cache engine and any verifier.
Read more →What three video-generation projects on TACC's Vista and Lonestar6 taught us about GPU infrastructure: memory hierarchy and OOM taxonomy, calibration-first batching, queue-driven multi-node fleets, asynchronous RL orchestration, telemetry that tells the truth, and the measured utilization evidence behind each lesson.
Read more →A long revision notebook for Research Scientist / GenAI loops focused on image generation, perceptual quality, and video processing: diffusion and flow models, transformer internals (attention variants, RoPE, KV cache), the text-to-image design space, evaluation metrics, color and HDR, classical CV, RL alignment, and the coding tier, all in one place.
Read more →Why classifier-free guidance breaks on flow models, and how a geometry-aware predictor-corrector fixes it, with a deep dive into text rendering quality and the adaptive schedule that makes it work.
Read more →An intuition-first deep dive into Rectified Flow with the core equations, geometric interpretation of trajectory straightness, and minimal PyTorch-style code snippets for training and sampling.
Read more →Why measuring quality in high dynamic range videos is fundamentally different from SDR, and what makes it one of the most challenging problems in perceptual quality research.
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