EVPO adaptively switches between critic-based and batch-mean advantage estimation using batch-level explained variance to provably achieve no greater variance than the better of PPO or GRPO at every step.
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DeltaRubric decomposes multimodal preference evaluation into self-generated planning and verification steps within a single model, producing large accuracy improvements on VL-RewardBench via multi-role reinforcement learning.
Faithful GRPO cuts inconsistent chain-of-thought reasoning in visual spatial tasks from 24.5% to 1.7% while raising visual grounding scores by 13% and final answer accuracy on seven benchmarks.
citing papers explorer
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EVPO: Explained Variance Policy Optimization for Adaptive Critic Utilization in LLM Post-Training
EVPO adaptively switches between critic-based and batch-mean advantage estimation using batch-level explained variance to provably achieve no greater variance than the better of PPO or GRPO at every step.
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DeltaRubric: Generative Multimodal Reward Modeling via Joint Planning and Verification
DeltaRubric decomposes multimodal preference evaluation into self-generated planning and verification steps within a single model, producing large accuracy improvements on VL-RewardBench via multi-role reinforcement learning.
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Faithful GRPO: Improving Visual Spatial Reasoning in Multimodal Language Models via Constrained Policy Optimization
Faithful GRPO cuts inconsistent chain-of-thought reasoning in visual spatial tasks from 24.5% to 1.7% while raising visual grounding scores by 13% and final answer accuracy on seven benchmarks.