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Reinforcement fine-tuning on Amazon Bedrock: Best practices

Reinforcement fine-tuning on Amazon Bedrock: Best practices

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You can use reinforcement Fine-Tuning (RFT) in Amazon Bedrock to customize Amazon Nova and supported open source models by defining what “good” looks like—no large labeled datasets required. By learning from reward signals rather than static examples, RFT delivers up to 66% accuracy gains over base models at reduced customization cost and complexity. This post covers best practices for RFT on Amazon Bedrock, from dataset design, reward function strategy, and hyperparameter tuning for use cases like code generation, […]

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How our digital devices are putting our right to privacy at risk

How our digital devices are putting our right to privacy at risk

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We live in a digitally connected world that has brought undeniable personal benefits. I can barely recall the pre-Google Maps era, but it was far less convenient to navigate unfamiliar places without a Siri-enabled smart phone (and/or Apple Car Play). We use fitness tracking apps, our home appliances are increasingly digitally connected, and many homes have security systems like Nest cameras or home assistants like Alexa or Amazon Echo. But what are we giving up for all this […]

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RL-ASL: A Dynamic Listening Optimization for TSCH Networks Using Reinforcement Learning

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Time Slotted Channel Hopping (TSCH) is a widely adopted Media Access Control (MAC) protocol within the IEEE 802.15.4e standard, designed to provide reliable and energy-efficient communication in Industrial Internet of Things (IIoT) networks. However, state-of-the-art TSCH schedulers rely on static slot allocations, resulting in idle listening and unnecessary power consumption under dynamic traffic conditions. This paper introduces RL-ASL, a reinforcement learning-driven adaptive listening framework that dynamically decides whether to activate or skip a scheduled listening slot based on […]

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I built OpenGrid : RL environment where your AI agent acts as a power grid operator (with live physics & renewables)

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Hello everyone, I wanted to share a project I am working on for a hackathon. It’s a reinforcement learning environment where an AI agent acts as a power grid operator. I’ve tried to keep physics and maths as real as possible. Github repo link : https://github.com/krishnagoyal099/Opengrid_env Live link : https://huggingface.co/spaces/K446/Opengrid I would really like to get your feedback on the physics modeling and reward structure, and also if anyone manages to solve the “hard” task! I am willing […]

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Learning Markov Processes as Sum-of-Square Forms for Analytical Belief Propagation

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Harnessing the predictive capability of Markov process models requires propagating probability density functions (beliefs) through the model. For many existing models however, belief propagation is analytically infeasible, requiring approximation or sampling to generate predictions. This paper proposes a functional modeling framework leveraging sparse Sum-of-Squares (SoS) forms for valid (conditional) density estimation. We study the theoretical restrictions of modeling conditional densities using the SoS form, and propose a novel functional form for addressing such limitations. The proposed architecture enables […]

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Lecture notes on Machine Learning applications for global fits

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These lecture notes provide a comprehensive framework for performing global statistical fits in high-energy physics using modern Machine Learning (ML) surrogates. We begin by reviewing the statistical foundations of model building, including the likelihood function, Wilks’ theorem, and profile likelihoods. Recognizing that the computational cost of evaluating model predictions often renders traditional minimization prohibitive, we introduce Boosted Decision Trees to approximate the log-likelihood function. The notes detail a robust ML workflow including efficient generation of training data with […]

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Robotics-AI-ML Project Ideas

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Hi, I am looking to do some project in robotics stimulation in the area of reinforcement learning. Can someone give me any good ideas as well as resources/platform to do so. I found one named Mojuco, but cannot find any good videos on that. submitted by /u/Southern_Reserve2609 [link] [comments]

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Can’t train a pixel-based PPO for Hopper environment

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Hi everyone. This is my first question in Reddit, so I do not know if this the place to publish it. I have been trying to train a PPO model to make a Hopper agent “walk”. I have implemented my own version of the PPO algorithm, so that I can modify the architecture more easily. I have done already a huge hyperparameter search (manually done), changed the reward function to an easier and also more complex one, chatted […]

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Motorola suddenly raises budget phone prices up to 50%—you can probably thank AI

Motorola suddenly raises budget phone prices up to 50%—you can probably thank AI

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Motorola announced a new mid-range phone yesterday, the 2026 Moto G Stylus. It’s not exactly a game changer unless you demand a stylus with your smartphone. Despite little in the way of upgrades, the new G Stylus will debut at $500, which is $100 more than last year’s version. It’s now clear that higher pricing will be a trend in Moto’s lineup. Without so much as a peep, Motorola has enacted price increases of up to 50 percent […]

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