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#preference optimization

SLIME: Stabilized Likelihood Implicit Margin Enforcement for Preference Optimization

Intermediate

Maksim Afanasyev, Illarion IovFeb 2arXiv

SLIME is a new way to train chatbots so they follow human preferences without forgetting how to write well.

#SLIME#preference optimization#DPO

HeartMuLa: A Family of Open Sourced Music Foundation Models

Intermediate

Dongchao Yang, Yuxin Xie et al.Jan 15arXiv

HeartMuLa is a family of open-source music AI models that can understand and generate full songs with clear lyrics and strong musical structure.

#music generation#audio tokenizer#residual vector quantization

Token-Level LLM Collaboration via FusionRoute

Intermediate

Nuoya Xiong, Yuhang Zhou et al.Jan 8arXiv

Big all-in-one language models are powerful but too expensive to run everywhere, while small specialists are cheaper but narrow.

#FusionRoute#token-level collaboration#expert routing

ThinkRL-Edit: Thinking in Reinforcement Learning for Reasoning-Centric Image Editing

Beginner

Hengjia Li, Liming Jiang et al.Jan 6arXiv

ThinkRL-Edit teaches an image editor to think first and draw second, which makes tricky, reasoning-heavy edits much more accurate.

#reasoning-centric image editing#reinforcement learning#chain-of-thought

Avatar Forcing: Real-Time Interactive Head Avatar Generation for Natural Conversation

Intermediate

Taekyung Ki, Sangwon Jang et al.Jan 2arXiv

This paper builds a real-time talking-listening head avatar that reacts naturally to your words, tone, nods, and smiles in about half a second.

#interactive avatar#talking head generation#causal diffusion forcing

Factorized Learning for Temporally Grounded Video-Language Models

Intermediate

Wenzheng Zeng, Difei Gao et al.Dec 30arXiv

This paper teaches video-language models to first find when the proof happens in a video and then answer with that proof, instead of mixing both steps together.

#temporal grounding#video-language models#evidence tokens

Adaptation of Agentic AI

Intermediate

Pengcheng Jiang, Jiacheng Lin et al.Dec 18arXiv

This paper organizes how AI agents learn and improve into one simple map with four roads: A1, A2, T1, and T2.

#agentic AI#adaptation#A1 A2 T1 T2