Routers in SMoE models form geometric alignments with their experts through shared gradient directions, enabling effective specialization that auxiliary load-balancing losses tend to disrupt.
Grouter: Decoupling routing from representation for accelerated moe training
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Routing topology in sparse Mixture-of-Experts models does not determine asymptotic language modeling perplexity; multiple variants including cosine-similarity routing achieve statistically equivalent performance.
SRaR attributes rubric items to specific steps via an LLM judge, normalizes per-step scores across rollouts, and combines them with outcome rewards via a decoupled advantage estimator, yielding 3.57-point accuracy gains on Qwen3-8B across math benchmarks.
citing papers explorer
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Routers Learn the Geometry of Their Experts: Geometric Coupling in Sparse Mixture-of-Experts
Routers in SMoE models form geometric alignments with their experts through shared gradient directions, enabling effective specialization that auxiliary load-balancing losses tend to disrupt.
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Equifinality in Mixture of Experts: Routing Topology Does Not Determine Language Modeling Quality
Routing topology in sparse Mixture-of-Experts models does not determine asymptotic language modeling perplexity; multiple variants including cosine-similarity routing achieve statistically equivalent performance.
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Step-wise Rubric Rewards for LLM Reasoning
SRaR attributes rubric items to specific steps via an LLM judge, normalizes per-step scores across rollouts, and combines them with outcome rewards via a decoupled advantage estimator, yielding 3.57-point accuracy gains on Qwen3-8B across math benchmarks.
- Leveraging Error Diversity in Group Rollouts for Reinforcement Learning