yuraedcel28@gmail.com

yuraedcel28@gmail.com

Training LLM Agents Just Got More Stable: Researchers Introduce StarPO-S and RAGEN to Tackle Multi-Turn Reasoning and Collapse in Reinforcement Learning

Large language models (LLMs) face significant challenges when trained as autonomous agents in interactive environments. Unlike static tasks, agent settings require sequential decision-making, cross-turn memory maintenance, and adaptation to stochastic environmental feedback. These capabilities are essential for developing effective planning…

Xiaomi introduced MiMo-7B: A Compact Language Model that Outperforms Larger Models in Mathematical and Code Reasoning through Rigorous Pre-Training and Reinforcement Learning

With rising demand for AI systems that can handle tasks involving multi-step logic, mathematical proofs, and software development, researchers have turned their attention toward enhancing models’ reasoning potential. This capability, once believed to be exclusive to human intelligence, is now…

DeepSeek-AI Released DeepSeek-Prover-V2: An Open-Source Large Language Model Designed for Formal Theorem, Proving through Subgoal Decomposition and Reinforcement Learning

Formal mathematical reasoning has evolved into a specialized subfield of artificial intelligence that requires strict logical consistency. Unlike informal problem solving, which allows for intuition and loosely defined heuristics, formal theorem proving relies on every step being fully described, precise,…

Salesforce AI Research Introduces New Benchmarks, Guardrails, and Model Architectures to Advance Trustworthy and Capable AI Agents

Salesforce AI Research has outlined a comprehensive roadmap for building more intelligent, reliable, and versatile AI agents. The recent initiative focuses on addressing foundational limitations in current AI systems—particularly their inconsistent task performance, lack of robustness, and challenges in adapting…