InfoTree casts intermediate state selection in tree search as monotone submodular maximization under fixed rollout budgets, yielding closed-form UUCB terms and lifting mixed-outcome ratios while outperforming flat GRPO and prior tree variants on nine benchmarks.
Jackpot: Optimal budgeted rejection sampling for extreme actor-policy discrep- ancy
5 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 5representative citing papers
Smaller models provide temporally correlated policy-level diversity that serves as structured exploration for training larger models in GRPO, yielding accuracy gains such as +8.8% on AIME 24 with reduced compute via the S2L-PO framework.
REFT improves Pass@1/8/64 in RLVR by uniform first-token sampling from top-N candidates across 0.5B-7B models and multiple difficulty levels.
A multimodal diffusion model trained on synthetic data enhances low-resolution EBSD and corrupted polarized light data, achieving near full-resolution performance with only 25% EBSD data.
EfficientRollout applies self-speculative decoding with quantized drafter induction and system-aware acceptance policies to cut RL rollout latency up to 19.6% while preserving final model quality.
citing papers explorer
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Maximizing Rollout Informativeness under a Fixed Budget: A Submodular View of Tree Search for Tool-Use Agentic Reinforcement Learning
InfoTree casts intermediate state selection in tree search as monotone submodular maximization under fixed rollout budgets, yielding closed-form UUCB terms and lifting mixed-outcome ratios while outperforming flat GRPO and prior tree variants on nine benchmarks.
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Smaller Models are Natural Explorers for Policy-Level Diversity in GRPO
Smaller models provide temporally correlated policy-level diversity that serves as structured exploration for training larger models in GRPO, yielding accuracy gains such as +8.8% on AIME 24 with reduced compute via the S2L-PO framework.
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Where Rollouts Begin: Low-Load, High-Leverage First-Token Diversification for RLVR
REFT improves Pass@1/8/64 in RLVR by uniform first-token sampling from top-N candidates across 0.5B-7B models and multiple difficulty levels.
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Multimodal Diffusion to Mutually Enhance Polarized Light and Low Resolution EBSD Data
A multimodal diffusion model trained on synthetic data enhances low-resolution EBSD and corrupted polarized light data, achieving near full-resolution performance with only 25% EBSD data.
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EfficientRollout: System-Aware Self-Speculative Decoding for RL Rollouts
EfficientRollout applies self-speculative decoding with quantized drafter induction and system-aware acceptance policies to cut RL rollout latency up to 19.6% while preserving final model quality.