Miner uses intrinsic policy uncertainty with token-level focal credit assignment and adaptive advantage calibration as a self-supervised reward to enable efficient RL training on positive homogeneous prompts, yielding up to 4.58 Pass@1 gains over GRPO on Qwen3 models.
Self-guided process reward optimization with redefined step-wise advantage for process reinforcement learning
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SD-GRPO extends GRPO by computing per-segment advantages via z-normalization of verifiable segment rewards, yielding gains on long-form VL tasks with varying semantic independence across segments.
CARL improves long-horizon agentic RL by using entropy as a proxy to identify critical states and selectively updating only on high-criticality actions, yielding stronger performance and efficiency.
TrOPD stabilizes on-policy distillation for LLMs with trust-region learning, outlier estimation, and off-policy guidance, outperforming prior OPD methods on reasoning and code benchmarks.
The paper unifies perspectives on Long CoT in reasoning LLMs by introducing a taxonomy, detailing characteristics of deep reasoning and reflection, and discussing emergence phenomena and future directions.
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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Miner:Mining Intrinsic Mastery for Data-Efficient RL in Large Reasoning Models
Miner uses intrinsic policy uncertainty with token-level focal credit assignment and adaptive advantage calibration as a self-supervised reward to enable efficient RL training on positive homogeneous prompts, yielding up to 4.58 Pass@1 gains over GRPO on Qwen3 models.
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SD-GRPO: Verifiable Segment Decomposition for Long-Form Vision-Language Generation
SD-GRPO extends GRPO by computing per-segment advantages via z-normalization of verifiable segment rewards, yielding gains on long-form VL tasks with varying semantic independence across segments.
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CARL: Criticality-Aware Agentic Reinforcement Learning
CARL improves long-horizon agentic RL by using entropy as a proxy to identify critical states and selectively updating only on high-criticality actions, yielding stronger performance and efficiency.
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Trust Region On-Policy Distillation
TrOPD stabilizes on-policy distillation for LLMs with trust-region learning, outlier estimation, and off-policy guidance, outperforming prior OPD methods on reasoning and code benchmarks.
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Towards Reasoning Era: A Survey of Long Chain-of-Thought for Reasoning Large Language Models
The paper unifies perspectives on Long CoT in reasoning LLMs by introducing a taxonomy, detailing characteristics of deep reasoning and reflection, and discussing emergence phenomena and future directions.
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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.
- Unleashing Implicit Rewards: Prefix-Value Learning for Distribution-Level Optimization
- From Reasoning to Agentic: Credit Assignment in Reinforcement Learning for Large Language Models