stable-rl-with-llms

Justifies token-level optimization objectives as first-order approximation to sequence rewards via Routing Replay, which fixes MoE routers during policy optimization to reduce training-inference discrepancy. Use when scaling RL training on large MoE language models.

Independent GitHub snapshot · Automated classification · Original documentation governs