ToolsRL trains MLLMs via a tool-specific then accuracy-focused RL curriculum to master visual tools for complex reasoning tasks.
Rl is neither a panacea nor a mirage: Understanding supervised vs
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RLVR induces sparse off-principal updates forming near-orthogonal shortcuts that degrade merging, addressed via Sensitivity-aware Resolving Merging using Fisher sensitivity, sparsification, and rescaling.
Excessive SFT reduces LLM plasticity for RL; Rejuvenation restores it via base-anchored fusion and targeted neuron resets, yielding better RL performance and OOD generalization.
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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Visual Reasoning through Tool-supervised Reinforcement Learning
ToolsRL trains MLLMs via a tool-specific then accuracy-focused RL curriculum to master visual tools for complex reasoning tasks.
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Sparsity Curse: Understanding RLVR Model Parameter Space from Model Merging
RLVR induces sparse off-principal updates forming near-orthogonal shortcuts that degrade merging, addressed via Sensitivity-aware Resolving Merging using Fisher sensitivity, sparsification, and rescaling.
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When RL Fails after SFT: Rejuvenating Model Plasticity for Robust SFT-to-RL Handoff
Excessive SFT reduces LLM plasticity for RL; Rejuvenation restores it via base-anchored fusion and targeted neuron resets, yielding better RL performance and OOD generalization.
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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.
- Rethinking Generalization in Reasoning SFT: A Conditional Analysis on Optimization, Data, and Model Capability