How MAGA learned to love AI safety - Transformer | Substack
<a href="https://news.google.com/rss/articles/CBMiqAFBVV95cUxNQUR3SWZmM2p6NTVYaDBLeFhjWkN6aGZ4ZGt0WG5mVnFNNmxiRE9XMzEyUkg0QzJkQUJxZGxJRTd2enFSdkhQYWtCcjA4NzBCdEN3VTBTQS1tTWR0a3JEeGhTT1RCQWEtSEhzaVRZQVBRbXA1ZGRrVmJUTmU1aUNKclY5cHZOVm5USzhHSGFKS01FYzNzMG1UUmp3aVFlbl9XZGJPWXp1aDY?oc=5" target="_blank">How MAGA learned to love AI safety</a> <font color="#6f6f6f">Transformer | Substack</font>
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v0.16.0
Axolotl v0.16.0 Release Notes We’re very excited to share this new packed release. We had ~80 new commits since v0.15.0 (March 6, 2026). Highlights Async GRPO — Asynchronous Reinforcement Learning Training ( #3486 ) Full support for asynchronous Group Relative Policy Optimization with vLLM integration. Includes async data producer with replay buffer, streaming partial-batch training, native LoRA weight sync to vLLM, and FP8 compatibility. Supports multi-GPU via FSDP1/FSDP2 and DeepSpeed ZeRO-3. Achieves up to 58% faster step times (1.59s/step vs 3.79s baseline on Qwen2-0.5B). Optimization Step Time Improvement Baseline 3.79s — + Batched weight sync 2.52s 34% faster + Liger kernel fusion 2.01s 47% faster + Streaming partial batch 1.79s 53% faster + Element chunking + re-roll fix (500 steps)
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