Engaging the AI community through building, research, and shared learning

Published: 1 day ago (February 2, 2026 at 02:51 PM EST)

1 min read

Source: Amazon Science

Engaging the AI Community

Advancing AI requires more than breakthrough models. It depends on communities of builders and researchers who experiment, test assumptions, and share what they learn. That belief is guiding how Amazon engages developers and academics around Amazon Nova, Amazon’s portfolio of AI offerings including …

Back to Blog

[Paper] Reward-free Alignment for Conflicting Objectives

Direct alignment methods are increasingly used to align large language models (LLMs) with human preferences. However, many real-world alignment problems involve...

[Paper] MEG-XL: Data-Efficient Brain-to-Text via Long-Context Pre-Training

Clinical brain-to-text interfaces are designed for paralysed patients who cannot provide extensive training recordings. Pre-training improves data-efficient gen...

[Paper] PixelGen: Pixel Diffusion Beats Latent Diffusion with Perceptual Loss

Pixel diffusion generates images directly in pixel space in an end-to-end manner, avoiding the artifacts and bottlenecks introduced by VAEs in two-stage latent ...

[Paper] RLAnything: Forge Environment, Policy, and Reward Model in Completely Dynamic RL System

We propose RLAnything, a reinforcement learning framework that dynamically forges environment, policy, and reward models through closed-loop optimization, ampli...

Engaging the AI Community

Related posts

[Paper] Reward-free Alignment for Conflicting Objectives

[Paper] MEG-XL: Data-Efficient Brain-to-Text via Long-Context Pre-Training

[Paper] PixelGen: Pixel Diffusion Beats Latent Diffusion with Perceptual Loss

[Paper] RLAnything: Forge Environment, Policy, and Reward Model in Completely Dynamic RL System