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Current LLMs struggle to effectively manage memory in multimodal, multi-participant settings, revealing critical gaps in their design.
FiberTune boosts VLA policy performance by preserving critical visual structure, resulting in up to 10.7 percentage points higher success rates on complex tasks.
Fine-grained control over reward signals unlocks significant gains in multi-trait essay scoring, outperforming standard policy optimization techniques.
A single normalization step turns Muon into Muon+, delivering consistent perplexity improvements in LLM pre-training.
