SAO stabilizes asynchronous RL for LLMs by replacing group-wise sampling with single-rollout updates, token-level importance sampling, and targeted value-model training, outperforming GRPO on reasoning and coding benchmarks.
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Qwen3.5-Omni scales an omnimodal model to hundreds of billions of parameters with 256k context, introduces ARIA for stable speech synthesis, and reports SOTA performance on 215 audio-visual benchmarks while adding multilingual and audio-visual coding capabilities.
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Single-Rollout Asynchronous Optimization for Agentic Reinforcement Learning
SAO stabilizes asynchronous RL for LLMs by replacing group-wise sampling with single-rollout updates, token-level importance sampling, and targeted value-model training, outperforming GRPO on reasoning and coding benchmarks.
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Qwen3.5-Omni Technical Report
Qwen3.5-Omni scales an omnimodal model to hundreds of billions of parameters with 256k context, introduces ARIA for stable speech synthesis, and reports SOTA performance on 215 audio-visual benchmarks while adding multilingual and audio-visual coding capabilities.