AI Briefing#kl-divergence
The latest AI and developer news about #kl-divergence, with the original source and a short summary.
The latest AI and developer news about #kl-divergence, with the original source and a short summary.
The study shows that best-of-N alignment closely approaches the theoretical maximum reward gain, while PPO and GRPO remain substantially suboptimal.
The number of annotators needed varies depending on the model's evaluation metric, so data collection strategies must be tailored to the target metric.
The author compares SFT, RL, and OPD from a distributional perspective and emphasizes the importance of policy-based sampling.
On Qwen3.6-35B-A3B, KLD and memory usage were compared across oQ, Q, MXFP, and UD MLX.
Re-measured the quality and long-context speed of Qwen 3.6-35B-A3B KV cache quantization on M5 Max.
The quantization levels of MLX Q and oQ were compared using KL divergence and RAM usage.