LLM

Large Language Models (LLMs) are no longer just text generators — they are becoming reasoners, capable of solving mathematical problems, logical puzzles, or planning tasks step by step. One of the key challenges is how to improve the quality of this reasoning. Traditional Reinforcement Learning (RL) rewards only the final outcome, but in complex reasoning it makes more sense to evaluate each intermediate step. This is called process-supervised RL (PSRL). ...

We’re used to hearing that AI sometimes “hallucinates” — making funny or random mistakes. Hallucinations are unintended errors caused by the limits of statistical prediction. But the new research goes further: it shows that AI can knowingly choose to lie when deception helps it achieve a goal. The publication Can LLMs Lie? takes us into a world where AI acts more like a strategic agent, capable of manipulating information to maximize outcomes. ...

We are in the midst of an AI gold rush, where companies are investing billions to build increasingly intelligent models. The final, crucial step in this process is often Reinforcement Learning (RL), the “finishing school” where an AI agent learns to master complex tasks through trial and error. However, this training process at an industrial scale is plagued by two crippling problems: crippling inefficiency and maddening complexity. It’s like trying to run a state-of-the-art factory where half the machines are always idle and every product requires a complete retooling of the assembly line. ...

In the era of Big Data, data has become an organization’s most valuable asset. However, access to it is often limited by a technical barrier: the need to use query languages like SQL. For years, analysts and engineers have dreamed of a system that would allow them to “talk” to a database in natural language. Text-to-SQL systems aim to realize this vision, but their path has been challenging. Older models, though promising, often failed in real-world scenarios: they were “brittle,” struggled with unseen database schemas, and required costly fine-tuning for each new domain. ...

In an era where Large Language Models (LLMs) like GPT-4 or Llama seem to understand the world, a fundamental challenge remains: how to teach them effectively and efficiently? The standard method is Supervised Fine-Tuning (SFT), which involves “feeding” the model thousands of examples of correct responses. However, as the groundbreaking paper “On the Generalization of SFT: A Reinforcement Learning Perspective with Reward Rectification” (arXiv:2508.05629) points out, SFT has a hidden flaw that limits its true potential. ...

In a world where artificial intelligence (AI) is solving increasingly complex problems, formal mathematical theorem proving remains one of the toughest challenges. It’s the Mount Everest of machine reasoning, demanding not only immense computational power but, above all, deep, logical deduction. The scientific paper “Goedel-Prover-V2: Scaling Formal Theorem Proving with Scaffolded Data Synthesis and Self-Correction” introduces a breakthrough system that elevates automated proving to a new level. 🤖 System Architecture At the heart of Goedel-Prover-V2 is an advanced language model, specially trained and adapted to work with proof assistants like Lean. The system’s architecture is based on a cyclical interaction between several key components: ...

The text-to-SQL task involves converting natural language questions into executable SQL queries on a relational database. While modern large language models (LLMs) excel at many generative tasks, generating correct and complex SQL queries remains challenging. In the paper RetrySQL: text-to-SQL training with retry data for self-correcting query generation, the authors introduce a training paradigm that teaches the model to self-monitor and correct its reasoning steps during generation, rather than relying solely on post-processing modules. ...