Reinforcement learning: Why alignment of numerics and MoE routing matter

Reinforcement learning at scale can become unstable when rollout generation and gradient training use separate implementations that differ in numerical precision, kernels, batching, parallelism, or operation order, causing them to assign different token probabilities despite identical model weights. Experiments cited with GLM 5.2 found that an unaligned trainer and inference engine produced a KL divergence of about 0.013, discarded roughly 45% of batches, and saw rewards collapse, while aligned calculations maintained zero KL and stable rewards. Mixture-of-Experts models add further risk because small rounding differences can alter expert routing and because even identical expert selections may yield divergent outputs if aggregation precision or execution order differs; a Qwen3.5-MoE test traced a substantial discrepancy to expert-output aggregation. Such failures may resemble issues with data, reward design, or learning rates, making probability-level validation important. The post argues that reliable RL infrastructure should co-develop training and rollout systems, align numerical operations, reuse routing decisions where needed, and measure full-model training–inference agreement through metrics such as KL divergence.

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