MASS-DPO derives a Plackett-Luce-specific log-determinant Fisher information objective to select non-redundant negative samples, matching or exceeding multi-negative DPO performance with substantially fewer negatives across four benchmarks and three model families.
Accelerating direct preference optimization with prefix sharing, 2024
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Schedule-level shared-prefix reuse decouples prefix and suffix passes in GRPO training to compute shared prefixes once, delivering up to 4.395x speedup and 59.1% HBM reduction while preserving numerical equivalence.
Tree Training serializes tree trajectories via DFS and uses redundancy-free partitioning to compute weighted per-token losses exactly once per token, achieving up to 6.2x training speedup on dense and MoE models.
citing papers explorer
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MASS-DPO: Multi-negative Active Sample Selection for Direct Policy Optimization
MASS-DPO derives a Plackett-Luce-specific log-determinant Fisher information objective to select non-redundant negative samples, matching or exceeding multi-negative DPO performance with substantially fewer negatives across four benchmarks and three model families.
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Schedule-Level Shared-Prefix Reuse for LLM RL Training
Schedule-level shared-prefix reuse decouples prefix and suffix passes in GRPO training to compute shared prefixes once, delivering up to 4.395x speedup and 59.1% HBM reduction while preserving numerical equivalence.
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Tree Training: Accelerating Agentic LLMs Training via Shared Prefix Reuse
Tree Training serializes tree trajectories via DFS and uses redundancy-free partitioning to compute weighted per-token losses exactly once per token, achieving up to 6.2x training speedup on dense and MoE models.