P2R decouples perception from reasoning in VLMs via a two-stage process and PRA-GRPO alternating RL training, reporting gains such as 93.2% on V-Star for the 4B model over its Qwen3-VL backbone.
Rethinking sample polarity in reinforcement learning with verifiable rewards
6 Pith papers cite this work. Polarity classification is still indexing.
6
Pith papers citing it
years
2026 6verdicts
UNVERDICTED 6representative citing papers
The cancellation hypothesis shows how rollout-level rewards produce token-level credit assignment in critic-free RL through cancellation of opposing signals on shared tokens, with empirical support and batching interventions that enhance performance.
NExt accelerates RLVR training for LLMs by nonlinearly extrapolating low-rank parameter trajectories extracted from LoRA runs.
EAPO uses policy entropy ratio to adaptively weight positive samples in RLVR for open-ended QA, claiming better diversity and stability than fixed-weight baselines on medical datasets.
Entropy polarity is a signed token-level quantity derived from a first-order approximation of entropy change that predicts whether RL updates expand or contract policy entropy in LLM fine-tuning, revealing an asymmetry between high- and low-probability tokens.
STARE applies surprisal-guided token-level advantage reweighting plus a target-entropy gate to stabilize entropy in GRPO RL for LLMs, yielding stable training and 4-8% gains on AIME24/25 over baselines.
citing papers explorer
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Perceive-to-Reason: Decoupling Perception and Reasoning for Fine-Grained Visual Reasoning
P2R decouples perception from reasoning in VLMs via a two-stage process and PRA-GRPO alternating RL training, reporting gains such as 93.2% on V-Star for the 4B model over its Qwen3-VL backbone.
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The Cancellation Hypothesis in Critic-Free RL: From Outcome Rewards to Token Credits
The cancellation hypothesis shows how rollout-level rewards produce token-level credit assignment in critic-free RL through cancellation of opposing signals on shared tokens, with empirical support and batching interventions that enhance performance.
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Low-rank Optimization Trajectories Modeling for LLM RLVR Acceleration
NExt accelerates RLVR training for LLMs by nonlinearly extrapolating low-rank parameter trajectories extracted from LoRA runs.
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EAPO: Entropy-Driven Adaptive Positive-Negative Sample Weighting for Policy Optimization in Open-Ended QA
EAPO uses policy entropy ratio to adaptively weight positive samples in RLVR for open-ended QA, claiming better diversity and stability than fixed-weight baselines on medical datasets.
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Entropy Polarity in Reinforcement Fine-Tuning: Direction, Asymmetry, and Control
Entropy polarity is a signed token-level quantity derived from a first-order approximation of entropy change that predicts whether RL updates expand or contract policy entropy in LLM fine-tuning, revealing an asymmetry between high- and low-probability tokens.
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STARE: Surprisal-Guided Token-Level Advantage Reweighting for Policy Entropy Stability
STARE applies surprisal-guided token-level advantage reweighting plus a target-entropy gate to stabilize entropy in GRPO RL for LLMs, yielding stable training and 4-8% gains on AIME24/25 over baselines.