AP-BMM approximates Pareto sets of layer-wise merged LLMs for accuracy-cost trade-offs via prior-guided asynchronous Bayesian optimization and reranking.
Gpqa: A graduate-level google-proof q&a benchmark
2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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StaRPO improves LLM reasoning by adding autocorrelation function and path efficiency stability metrics to RL policy optimization, yielding higher accuracy and fewer logic errors on reasoning benchmarks.
citing papers explorer
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AP-BMM: Approximating Capability-Cost Pareto Sets of LLMs via Asynchronous Prior-Guided Bayesian Model Merging
AP-BMM approximates Pareto sets of layer-wise merged LLMs for accuracy-cost trade-offs via prior-guided asynchronous Bayesian optimization and reranking.
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StaRPO: Stability-Augmented Reinforcement Policy Optimization
StaRPO improves LLM reasoning by adding autocorrelation function and path efficiency stability metrics to RL policy optimization, yielding higher accuracy and fewer logic errors on reasoning benchmarks.