NVIDIA Researchers Propose Reinforcement Learning Pretraining (RLP): Reinforcement as a Pretraining Objective for Building Reasoning During Pretraining
Why this matters technically: unlike prior “reinforcement pretraining” variants that rely on sparse, binary correctness signals or proxy filters, RLP’s dense, verifier-free reward attaches position-wise credit wherever thinking improves prediction,…
7 LLM Generation Parameters—What They Do and How to Tune Them?
Tuning LLM outputs is largely a decoding problem: you shape the model’s next-token distribution with a handful of sampling controls—max tokens (caps response length under the model’s context limit), temperature…
ServiceNow AI Research Releases DRBench, a Realistic Enterprise Deep-Research Benchmark
ServiceNow Research has released DRBench, a benchmark and runnable environment to evaluate “deep research” agents on open-ended enterprise tasks that require synthesizing facts from both public web and private organizational…
Meta’s ARE + Gaia2 Set a New Bar for AI Agent Evaluation under Asynchronous, Event-Driven Conditions
Meta AI has introduced Agents Research Environments (ARE), a modular simulation stack for creating and running agent tasks, and Gaia2, a follow-up benchmark to GAIA that evaluates agents in dynamic,…
Ivy Framework Agnostic Machine Learning Build, Transpile, and Benchmark Across All Major Backends
In this tutorial, we explore Ivy’s remarkable ability to unify machine learning development across frameworks. We begin by writing a fully framework-agnostic neural network that runs seamlessly on NumPy, PyTorch,…
Microsoft AI Debuts MAI-Image-1: An In-House Text-to-Image Model that Enters LMArena’s Top-10
Microsoft AI introduced MAI-Image-1, its first image generation model developed entirely in-house at Microsoft. The model has debuted in the Top-10 of the LMArena text-to-image leaderboard (as of Oct 13,…
How to Evaluate Your RAG Pipeline with Synthetic Data?
Evaluating LLM applications, particularly those using RAG (Retrieval-Augmented Generation), is crucial but often neglected. Without proper evaluation, it’s almost impossible to confirm if your system’s retriever is effective, if the…
SwiReasoning: Entropy-Driven Alternation of Latent and Explicit Chain-of-Thought for Reasoning LLMs
SwiReasoning is a decoding-time framework that lets a reasoning LLM decide when to think in latent space and when to write explicit chain-of-thought, using block-wise confidence estimated from entropy trends…
Google Introduces Speech-to-Retrieval (S2R) Approach that Maps a Spoken Query Directly to an Embedding and Retrieves Information without First Converting Speech to Text
Google AI Research team has brought a production shift in Voice Search by introducing Speech-to-Retrieval (S2R). S2R maps a spoken query directly to an embedding and retrieves information without first…
A Coding Implementation of Secure AI Agent with Self-Auditing Guardrails, PII Redaction, and Safe Tool Access in Python
In this tutorial, we explore how to secure AI agents in practical, hands-on ways using Python. We focus on building an intelligent yet responsible agent that adheres to safety rules…
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