GLM-5.3-Flash RL training pushed to 1M-token context in SkyRL, 2.1x faster at 256K
AndrewLBeam · x · 2026-10-23
Andrew Beam's team partnered with Anyscale to push GLM-5.3-Flash training to 1M tokens in SkyRL — 64x longer context — plus 2.1x faster training steps at 256K. The culprit: the sparse attention implementation 'wasn't so sparse.' Fixes have been contributed back to open source.
Related event: GLM-5.3-Flash RL Training Context Scaled to 1M Tokens, 2.1x Faster(3 posts)→
More from Infra
- Claude Models on Google Cloud (Anthropic managed) is now generally available — claudeai · 2026-10-23
- Scaling GLM-5.3-Flash training to 1M token context, 64x over Megatron baseline — AndrewLBeam · 2026-10-23
- Perplexity's Portable Computer lands on AMD Ryzen AI Max, running local 27B model — AravSrinivas · 2026-10-23
- Liquid AI Launches RIPL: Ultra-Low-Latency Inference Engine With Sub-50ms End-to-End Responses — JosephJacks_ · 2026-10-23
- OpenJet claims Qwen3.8-27B coding runs 1.9x faster than llama.cpp on one RTX 3090 — JeremyCMorgan · 2026-10-23
- Jensen Huang lays out NVIDIA's full Vera Rubin agentic system: NVL72, MGX, CMX and RTX — rohanpaul_ai · 2026-10-23