Hugging Face Trending Papers June 14, 2026

Localizing Credit at the Divergence: Path-Conditioned Self-Distillation for LLM Reasoning

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Reinforcement learning from verifiable rewards assigns a single scalar to each rollout, leaving token-level credit assignment underspecified in long reasoning traces. On-policy self-distillation addresses this by letting the same model act as a teacher conditioned on privileged information, producing a dense per-token signal.

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arXiv AI

Jun 16

Localizing Credit at the Divergence: Path-Conditioned Self-Distillation for LLM Reasoning

arXiv:2606. 15576v1 Announce Type: cross Abstract: Reinforcement learning from verifiable rewards assigns a single scalar to each rollout, leaving token-level credit assignment underspecified in long reasoning traces.

By Yu Li, Shu Hong, Tian Lan

llms reinforcement-learning efficiency benchmarks

Hugging Face Trending Papers

Jun 17

Learning from Own Solutions: Self-Conditioned Credit Assignment for Reinforcement Learning with Verifiable Rewards

Reinforcement learning with verifiable rewards (RLVR) has driven substantial progress in training LLMs for reasoning tasks, but representative methods such as GRPO assign uniform credit across all tokens, wasting gradient on routine tokens while under-crediting pivotal reasoning steps. Existing token-level credit assignment methods require resources beyond the model's own rollouts.

llms agents reinforcement-learning efficiency benchmarks

arXiv AI

5d ago

Beyond Outcome Rewards: Step-Level Self-Distilled Policy Optimization for Deep Search Agents

arXiv:2608. 12764v1 Announce Type: cross Abstract: Deep search agents operate over trajectories spanning dozens of steps, yet standard reinforcement learning provides only a single outcome reward per trajectory, which is far too sparse for effective credit assignment.

By Haoze Wu, Chuqiao Kuang, Tianyi Zhuang, Xiaoguang Li

agents reinforcement-learning efficiency

arXiv AI

Jun 18

Learning from Own Solutions: Self-Conditioned Credit Assignment for Reinforcement Learning with Verifiable Rewards

arXiv:2606. 18810v1 Announce Type: cross Abstract: Reinforcement learning with verifiable rewards (RLVR) has driven substantial progress in training LLMs for reasoning tasks, but representative methods such as GRPO assign uniform credit across all tokens, wasting gradient on routine tokens while under-crediting pivotal reasoning steps.

By Yingyu Shan, Yuhang Guo, Zihao Cheng, Zeming Liu, Xiangrong Zhu, Xinyi Wang, Jiashu Yao, Wei Lin, Hongru Wang, Heyan Huang

llms agents reinforcement-learning efficiency benchmarks

arXiv Machine Learning

Jul 22

H$^2$SD: Hybrid Hindsight Self-Distillation

arXiv:2607. 18955v1 Announce Type: new Abstract: Reinforcement learning with verifiable rewards (RLVR) has substantially improved the reasoning capabilities of large language models on tasks such as mathematical reasoning and code generation.

By Qiye Cai, Yichuan Ma, Linyang Li, Peiji Li, Yongkang Chen, Qipeng Guo, Yicheng Zou, Tao Gui, Xiaocheng Feng, Bing Qin

llms reinforcement-learning efficiency benchmarks

arXiv AI

Jun 12

Keep Policy Gradient in Charge: Sibling-Guided Credit Distillation for Long-Horizon Tool-Use Agents

arXiv:2606. 12634v1 Announce Type: cross Abstract: Long-horizon tool-use reinforcement learning can learn from outcome verification, but its trajectory-level advantage is broadcast across many reasoning, API, and answer tokens.

By Tianyu Ding, Jianhong Xin, Juan Pablo De la Cruz Weinstein

llms agents reinforcement-learning efficiency

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Localizing Credit at the Divergence: Path-Conditioned Self-Distillation for LLM Reasoning

Learning from Own Solutions: Self-Conditioned Credit Assignment for Reinforcement Learning with Verifiable Rewards

Beyond Outcome Rewards: Step-Level Self-Distilled Policy Optimization for Deep Search Agents

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H$^2$SD: Hybrid Hindsight Self-Distillation

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