SPEED-Bench is a new standardized benchmark for speculative decoding that supplies semantically diverse qualitative data and throughput-oriented splits across concurrency levels, integrated with vLLM and TensorRT-LLM.
Omni-think: Scaling cross-domain generalization in llms via multi-task rl with hybrid rewards
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SPARD dynamically tunes multi-objective reward weights and data importance in LLM reinforcement learning alignment using a self-paced curriculum driven by reward dynamics and data utility.
A survey compiling RL methods, challenges, data resources, and applications for enhancing reasoning in large language models and large reasoning models since DeepSeek-R1.
citing papers explorer
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SPEED-Bench: A Unified and Diverse Benchmark for Speculative Decoding
SPEED-Bench is a new standardized benchmark for speculative decoding that supplies semantically diverse qualitative data and throughput-oriented splits across concurrency levels, integrated with vLLM and TensorRT-LLM.
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SPARD: Self-Paced Curriculum for RL Alignment via Integrating Reward Dynamics and Data Utility
SPARD dynamically tunes multi-objective reward weights and data importance in LLM reinforcement learning alignment using a self-paced curriculum driven by reward dynamics and data utility.
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A Survey of Reinforcement Learning for Large Reasoning Models
A survey compiling RL methods, challenges, data resources, and applications for enhancing reasoning in large language models and large reasoning models since DeepSeek-R1.