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#verifiable rewards

Tool-R0: Self-Evolving LLM Agents for Tool-Learning from Zero Data

Beginner
Emre Can Acikgoz, Cheng Qian et al.Feb 24arXiv

Tool-R0 teaches a language model to use software tools (like APIs) with zero human-made training data.

#self-play reinforcement learning#tool calling#function calling

Llama-3.1-FoundationAI-SecurityLLM-Reasoning-8B Technical Report

Beginner
Zhuoran Yang, Ed Li et al.Jan 28arXiv

This paper introduces Foundation-Sec-8B-Reasoning, a small (8 billion parameter) AI model that is trained to “think out loud” before answering cybersecurity questions.

#native reasoning#cybersecurity LLM#chain-of-thought

The Flexibility Trap: Why Arbitrary Order Limits Reasoning Potential in Diffusion Language Models

Beginner
Zanlin Ni, Shenzhi Wang et al.Jan 21arXiv

Diffusion language models can write tokens in any order, but that freedom can accidentally hurt their ability to reason well.

#diffusion language model#arbitrary order generation#autoregressive training

STEP3-VL-10B Technical Report

Beginner
Ailin Huang, Chengyuan Yao et al.Jan 14arXiv

STEP3-VL-10B is a small (10 billion parameters) open multimodal model that sees images and reads text, yet scores like much larger models.

#multimodal foundation model#unified pre-training#perception encoder