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Separate Your Power BI Versions: How to Change Icons in Windows 11 - BI Insight
In a previous blog, we explored the different versions of Power BI Desktop, including the standard Power BI Desktop and Power BI Desktop RS, tailored for Power BI Report Server. In another blog, we examined the two variations of the standard Power BI Desktop: the Microsoft Store version and the Downloaded version. We also discussed scenarios where having both versions installed side-by-side might … Continue reading Separate Your Power BI Versions: How to Change Icons in Windows 11
Meet OREO (Offline REasoning Optimization): An Offline Reinforcement Learning Method for Enhancing LLM Multi-Step Reasoning - MarkTechPost
Large Language Models (LLMs) have demonstrated impressive proficiency in numerous tasks, but their ability to perform multi-step reasoning remains a significant challenge. This limitation becomes particularly evident in complex scenarios such as mathematical problem-solving, embodied agent control, and web navigation. Traditional Reinforcement Learning (RL) methods, like Proximal Policy Optimization (PPO), have been applied to address this issue but often come with high computational and data costs, making them less practical. Likewise, methods such as Direct Preference Optimization (DPO), while effective for aligning models with human preferences, struggle with multi-step reasoning tasks. DPO’s reliance on pairwise preference data and uniform token
OpenAI Researchers Propose 'Deliberative Alignment': A Training Approach that Teaches LLMs to Explicitly Reason through Safety Specifications before Producing an Answer - MarkTechPost
The widespread use of large-scale language models (LLMs) in safety-critical areas has brought forward a crucial challenge: how to ensure their adherence to clear ethical and safety guidelines. Existing alignment techniques, such as supervised fine-tuning (SFT) and reinforcement learning from human feedback (RLHF), have limitations. Models can still produce harmful content when manipulated, refuse legitimate requests, or struggle to handle unfamiliar scenarios. These issues often stem from the implicit nature of current safety training, where models infer standards indirectly from data rather than learning them explicitly. Additionally, models generally lack the ability to deliberate on complex prompts, which limits their