Papers159

#GRPO

LatentChem: From Textual CoT to Latent Thinking in Chemical Reasoning

LatentChem lets AI do chemistry thinking quietly inside continuous vectors instead of writing long step-by-step sentences.

#Latent reasoning#Chain-of-Thought#Chemical LLM

Not triaged yet

Self-Improving Multilingual Long Reasoning via Translation-Reasoning Integrated Training

Intermediate

Junxiao Liu, Zhijun Wang et al.Feb 5arXiv

TRIT is a new training method that teaches AI to translate and think at the same time so it can solve hard problems in many languages without extra helper models.

#multilingual reasoning#translation-reasoning integration#self-translation

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Reinforcement World Model Learning for LLM-based Agents

Intermediate

Xiao Yu, Baolin Peng et al.Feb 5arXiv

Large language models are great at words, but they struggle to predict what will happen after they act in a changing world.

#Reinforcement World Model Learning#world modeling#LLM agents

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Unveiling Implicit Advantage Symmetry: Why GRPO Struggles with Exploration and Difficulty Adaptation

Intermediate

Zhiqi Yu, Zhangquan Chen et al.Feb 5arXiv

The paper finds a hidden symmetry inside GRPO’s advantage calculation that accidentally stops models from exploring new good answers and from paying the right attention to easy versus hard problems at the right times.

#GRPO#GRAE#A-GRAE

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Multi-Task GRPO: Reliable LLM Reasoning Across Tasks

Intermediate

Shyam Sundhar Ramesh, Xiaotong Ji et al.Feb 5arXiv

Large language models are usually trained to get good at one kind of reasoning, but real life needs them to be good at many things at once.

#Multi-Task Learning#GRPO#Reinforcement Learning Post-Training

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ProAct: Agentic Lookahead in Interactive Environments

Intermediate

Yangbin Yu, Mingyu Yang et al.Feb 5arXiv

ProAct teaches AI agents to think ahead accurately without needing expensive search every time they act.

#ProAct#GLAD#MC-Critic

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Length-Unbiased Sequence Policy Optimization: Revealing and Controlling Response Length Variation in RLVR

Intermediate

Fanfan Liu, Youyang Yin et al.Feb 5arXiv

The paper discovers that popular RLVR methods for training language and vision-language models secretly prefer certain answer lengths, which can hurt learning.

#LUSPO#RLVR#GRPO

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Reinforced Attention Learning

Intermediate

Bangzheng Li, Jianmo Ni et al.Feb 4arXiv

This paper teaches AI to pay attention better by training its focus, not just its words.

#Reinforced Attention Learning#attention policy#multimodal LLM

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Rethinking the Trust Region in LLM Reinforcement Learning

Intermediate

Penghui Qi, Xiangxin Zhou et al.Feb 4arXiv

The paper shows that the popular PPO method for training language models is unfair to rare words and too gentle with very common words, which makes learning slow and unstable.

#Reinforcement Learning#Proximal Policy Optimization#Trust Region

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Privileged Information Distillation for Language Models

Intermediate

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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