Papers181

#GRPO

Privileged Information Distillation for Language Models

Emiliano Penaloza, Dheeraj Vattikonda et al.Feb 4arXiv

The paper shows how to train a language model with special extra hints (privileged information) during practice so it can still do well later without any hints.

#Privileged Information#Knowledge Distillation#π-Distill

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Skin Tokens: A Learned Compact Representation for Unified Autoregressive Rigging

Intermediate

Jia-peng Zhang, Cheng-Feng Pu et al.Feb 4arXiv

Rigging 3D characters is a bottleneck: making bones and skin weights by hand is slow and tricky, and past automatic tools often guess the skin weights poorly.

#auto-rigging#skinning weights#SkinTokens

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WideSeek-R1: Exploring Width Scaling for Broad Information Seeking via Multi-Agent Reinforcement Learning

Intermediate

Zelai Xu, Zhexuan Xu et al.Feb 4arXiv

WideSeek-R1 teaches a small 4B-parameter language model to act like a well-run team: one leader plans, many helpers work in parallel, and everyone learns together with reinforcement learning.

#width scaling#multi-agent reinforcement learning#orchestration

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Beyond Unimodal Shortcuts: MLLMs as Cross-Modal Reasoners for Grounded Named Entity Recognition

Intermediate

Jinlong Ma, Yu Zhang et al.Feb 4arXiv

The paper teaches multimodal large language models (MLLMs) to stop guessing from just text or just images and instead check both together before answering.

#GMNER#Multimodal Large Language Models#Modality Bias

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AgentArk: Distilling Multi-Agent Intelligence into a Single LLM Agent

Intermediate

Yinyi Luo, Yiqiao Jin et al.Feb 3arXiv

AgentArk teaches one language model to think like a whole team of models that debate, so it can solve tough problems quickly without running a long, expensive debate at answer time.

#multi-agent distillation#process reward model#GRPO

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Reasoning Cache: Continual Improvement Over Long Horizons via Short-Horizon RL

Intermediate

Ian Wu, Yuxiao Qu et al.Feb 3arXiv

Reasoning Cache (RC) is a new way for AI to think in steps: it writes some thoughts, makes a short summary, throws away the long thoughts, and then keeps going using only the summary.

#Reasoning Cache#iterative decoding#summary-conditioned reasoning

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Search-R2: Enhancing Search-Integrated Reasoning via Actor-Refiner Collaboration

Intermediate

Bowei He, Minda Hu et al.Feb 3arXiv

This paper teaches AI to look things up on the web and fix its own mistakes mid-thought instead of starting over from scratch.

#search-integrated reasoning#reinforcement learning#credit assignment

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Learning Query-Specific Rubrics from Human Preferences for DeepResearch Report Generation

Intermediate

Changze Lv, Jie Zhou et al.Feb 3arXiv

DeepResearch agents write long, evidence-based reports, but teaching and grading them is hard because there is no single 'right answer' to score against.

#DeepResearch#query-specific rubrics#human preference learning

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SWE-Master: Unleashing the Potential of Software Engineering Agents via Post-Training

Intermediate

Huatong Song, Lisheng Huang et al.Feb 3arXiv

SWE-Master is a fully open, step-by-step recipe for turning a regular coding model into a strong software-fixing agent that works across many steps, files, and tests.

#SWE-Master#software engineering agent#long-horizon SFT

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On the Entropy Dynamics in Reinforcement Fine-Tuning of Large Language Models

Intermediate

Shumin Wang, Yuexiang Xie et al.Feb 3arXiv

The paper builds a simple, math-light rule to predict whether training makes a language model more open-minded (higher entropy) or more sure of itself (lower entropy).

#reinforcement fine-tuning#entropy dynamics#GRPO

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Self-Hinting Language Models Enhance Reinforcement Learning

Intermediate

Baohao Liao, Hanze Dong et al.Feb 3arXiv

When rewards are rare, a popular training method for language models (GRPO) often stops learning because every try in a group gets the same score, so there is nothing to compare.

#reinforcement learning#GRPO#self-hinting

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CoBA-RL: Capability-Oriented Budget Allocation for Reinforcement Learning in LLMs

Intermediate

Zhiyuan Yao, Yi-Kai Zhang et al.Feb 3arXiv

Large language models learn better when we spend more practice time on the right questions at the right moments.

#Reinforcement Learning#RLVR#GRPO

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