Papers25

#curriculum learning

TTCS: Test-Time Curriculum Synthesis for Self-Evolving

Chengyi Yang, Zhishang Xiang et al.Jan 30arXiv

TTCS is a way for a model to teach itself during the test by first making easier practice questions that are similar to the real hard question and then learning from them.

#test-time training#test-time reinforcement learning#curriculum learning

Teaching Models to Teach Themselves: Reasoning at the Edge of Learnability

Intermediate

Shobhita Sundaram, John Quan et al.Jan 26arXiv

This paper teaches a model to be its own teacher so it can climb out of a learning plateau on very hard math problems.

#meta-reinforcement learning#teacher-student self-play#grounded rewards

DARC: Decoupled Asymmetric Reasoning Curriculum for LLM Evolution

Intermediate

Shengda Fan, Xuyan Ye et al.Jan 20arXiv

DARC teaches big language models to get smarter by splitting training into two calm, well-organized steps instead of one chaotic loop.

#DARC#self-play#curriculum learning

ShapeR: Robust Conditional 3D Shape Generation from Casual Captures

Intermediate

Yawar Siddiqui, Duncan Frost et al.Jan 16arXiv

ShapeR builds clean, correctly sized 3D objects from messy, casual phone or glasses videos by using images, camera poses, sparse SLAM points, and short text captions together.

#ShapeR#3D reconstruction#object-centric

Dr. Zero: Self-Evolving Search Agents without Training Data

Intermediate

Zhenrui Yue, Kartikeya Upasani et al.Jan 11arXiv

Dr. Zero is a pair of AI agents (a Proposer and a Solver) that teach each other to do web-search-based reasoning without any human-written training data.

#Dr. Zero#self-evolution#proposer-solver

Solar Open Technical Report

Intermediate

Sungrae Park, Sanghoon Kim et al.Jan 11arXiv

Solar Open is a giant bilingual AI (102 billion parameters) that focuses on helping underserved languages like Korean catch up with English-level AI quality.

#Solar Open#Mixture-of-Experts#bilingual LLM

WebGym: Scaling Training Environments for Visual Web Agents with Realistic Tasks

Intermediate

Hao Bai, Alexey Taymanov et al.Jan 5arXiv

WebGym is a giant practice world (almost 300,000 tasks) that lets AI web agents learn on real, ever-changing websites instead of tiny, fake ones.

#WebGym#visual web agents#vision-language models

DreamID-V:Bridging the Image-to-Video Gap for High-Fidelity Face Swapping via Diffusion Transformer

Intermediate

Xu Guo, Fulong Ye et al.Jan 4arXiv

DreamID-V is a new AI method that swaps faces in videos while keeping the body movements, expressions, lighting, and background steady and natural.

#video face swapping#image face swapping#diffusion transformer

Youtu-LLM: Unlocking the Native Agentic Potential for Lightweight Large Language Models

Intermediate

Junru Lu, Jiarui Qin et al.Dec 31arXiv

Youtu-LLM is a small (1.96B) language model that was trained from scratch to think, plan, and act like an agent instead of just copying bigger models.

#lightweight LLM#agentic mid-training#trajectory data

GenEnv: Difficulty-Aligned Co-Evolution Between LLM Agents and Environment Simulators

Intermediate

Jiacheng Guo, Ling Yang et al.Dec 22arXiv

GenEnv is a training system where a student AI and a teacher simulator grow together by exchanging tasks and feedback.

#GenEnv#co-evolutionary learning#difficulty-aligned curriculum

Puzzle Curriculum GRPO for Vision-Centric Reasoning

Intermediate

Ahmadreza Jeddi, Hakki Can Karaimer et al.Dec 16arXiv

This paper teaches vision-language models to reason about pictures using puzzles instead of expensive human labels.

#vision-language models#reinforcement learning#group-relative policy optimization

Achieving Olympia-Level Geometry Large Language Model Agent via Complexity Boosting Reinforcement Learning

Intermediate

Haiteng Zhao, Junhao Shen et al.Dec 11arXiv

This paper builds InternGeometry, a large language model agent that solves Olympiad-level geometry by talking to a math engine, remembering what worked, and trying smart new ideas.

#InternGeometry#geometry theorem proving#auxiliary constructions

1 2 3