Papers776

Rethinking Selective Knowledge Distillation

Almog Tavor, Itay Ebenspanger et al.Feb 1arXiv

The paper studies how to teach a smaller language model using a bigger one by only focusing on the most useful bits instead of everything.

#knowledge distillation#selective distillation#student entropy

PromptRL: Prompt Matters in RL for Flow-Based Image Generation

Intermediate

Fu-Yun Wang, Han Zhang et al.Feb 1arXiv

PromptRL teaches a language model to rewrite prompts while a flow-based image model learns to draw, and both are trained together using the same rewards.

#PromptRL#flow matching#reinforcement learning

Balancing Understanding and Generation in Discrete Diffusion Models

Intermediate

Yue Liu, Yuzhong Zhao et al.Feb 1arXiv

This paper introduces XDLM, a single model that blends two popular diffusion styles (masked and uniform) so it both understands and generates text and images well.

#XDLM#discrete diffusion#stationary noise kernel

Beyond Pixels: Visual Metaphor Transfer via Schema-Driven Agentic Reasoning

Intermediate

Yu Xu, Yuxin Zhang et al.Feb 1arXiv

This paper teaches AI to copy the hidden idea inside a picture (a visual metaphor) and reuse that idea on a brand‑new subject.

#visual metaphor#metaphor transfer#schema grammar

Good SFT Optimizes for SFT, Better SFT Prepares for Reinforcement Learning

Intermediate

Dylan Zhang, Yufeng Xu et al.Feb 1arXiv

The paper shows that a model that looks great after supervised fine-tuning (SFT) can actually do worse after the same reinforcement learning (RL) than a model that looked weaker at SFT time.

#Supervised Fine-Tuning#Reinforcement Learning#Distribution Mismatch

LRAgent: Efficient KV Cache Sharing for Multi-LoRA LLM Agents

Intermediate

Hyesung Jeon, Hyeongju Ha et al.Feb 1arXiv

Multi-agent LLM systems often use LoRA adapters so each agent has a special role, but they all rebuild almost the same KV cache, wasting memory and time.

#LoRA#Multi-LoRA#KV cache

Sparse Reward Subsystem in Large Language Models

Intermediate

Guowei Xu, Mert Yuksekgonul et al.Feb 1arXiv

The paper discovers a tiny, special group of neurons inside large language models (LLMs) that act like a reward system in the human brain.

#value neurons#dopamine neurons#reward prediction error

Green-VLA: Staged Vision-Language-Action Model for Generalist Robots

Intermediate

I. Apanasevich, M. Artemyev et al.Jan 31arXiv

Green-VLA is a step-by-step training recipe that teaches one model to see, understand language, and move many kinds of robots safely and efficiently.

#Vision-Language-Action#Unified Action Space#Multi-embodiment Pretraining

Adaptive Ability Decomposing for Unlocking Large Reasoning Model Effective Reinforcement Learning

Intermediate

Zhipeng Chen, Xiaobo Qin et al.Jan 31arXiv

This paper teaches a model to make its own helpful hints (sub-questions) and then use those hints to learn better with reinforcement learning that checks answers automatically.

#RLVR#Large Reasoning Models#Sub-question Guidance

Decouple Searching from Training: Scaling Data Mixing via Model Merging for Large Language Model Pre-training

Intermediate

Shengrui Li, Fei Zhao et al.Jan 31arXiv

Training big language models works best when you mix the right kinds of data (general, math, code), but finding the best mix used to be slow and very expensive.

#data mixture optimization#model merging#weighted model merging

Position: Agentic Evolution is the Path to Evolving LLMs

Intermediate

Minhua Lin, Hanqing Lu et al.Jan 30arXiv

Big AI models do great in the lab but stumble in the real world because the world keeps changing.

#agentic evolution#A-Evolve#deployment-time adaptation

VoxServe: Streaming-Centric Serving System for Speech Language Models

Intermediate

Keisuke Kamahori, Wei-Tzu Lee et al.Jan 30arXiv

VoxServe is a new serving system that makes voice AIs respond fast and smoothly when streaming audio to users.

#Speech Language Models#streaming#Time-To-First-Audio

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