From: aidotengineer
Vision for AI Development
The long-term vision for AI development at Quen involves building a generalist model and a generalist agent [00:00:28]. This extends beyond merely creating instruction-tuned models, aiming for AI that can become progressively smarter through advanced techniques like reinforcement learning [00:02:08].
Reinforcement Learning (RL) for Enhanced Performance
Reinforcement learning (RL) has shown significant promise in increasing model performance, particularly in reasoning tasks such as mathematics and coding [00:02:26]. This approach can lead to consistent performance increases, turning models into more capable reasoning agents [00:02:36].
Evolution of Model Architectures
The future trend in AI models is believed to belong to Mixture-of-Experts (MOE) models [00:12:18]. These models are designed to be efficient while remaining highly effective.
Key Areas for Future Development
Enhancing Training Methodologies
Significant improvements in training are still possible [00:21:17]. Areas of focus include:
- Data Quality and Inclusion: Incorporating more high-quality data that has not yet been utilized or thoroughly cleaned [00:21:28].
- Multimodal Data: Utilizing multimodal data to enhance model capabilities across different tasks and domains [00:21:37].
- Synthetic Data: Exploring the use of synthetic data in training [00:21:46].
- Novel Training Methods: Moving beyond traditional next-token prediction to explore different training methods for pre-training, potentially including reinforcement learning in this initial phase [00:21:52].
Scaling Laws and Compute
The landscape of scaling laws is evolving [00:22:12]. While past scaling efforts focused on model sizes and pre-training data, the current emphasis is on compute in reinforcement learning [00:22:23]. The goal is to develop models capable of long-horizon reasoning with continuous environment feedback, enabling them to become smarter through inference-time scaling [00:22:28].
Context Length Scaling
A key focus is to significantly scale context length [00:23:02]. Current efforts aim to resolve issues with 1 million tokens and then progress towards 10 million tokens, with an ultimate goal of achieving infinite context [00:23:07]. Most models are expected to support at least 1 million tokens within the current year [00:23:19].
Modality Scaling
While scaling on modalities may not directly increase intelligence, it significantly enhances a model’s capabilities and productivity [00:23:30]. This includes:
- Multimodal Input and Output: The aim is to build “omni models” that can accept multiple modalities as inputs (text, vision, audio) and generate multiple modalities as outputs (text, audio, with future aims for high-quality images and videos) [00:13:51], [00:14:33].
- GUI Agents: Vision language understanding is crucial for developing GUI agents and enabling computer use tasks [00:23:43].
- Unified Understanding and Generation: Work is being done to unify understanding and generation, for example, enabling image understanding and generation within the same model [00:24:07].
Transition to Agent Training
The overall shift in focus is from simply training models to training agents [00:24:36]. This involves scaling not only with pre-training but also with reinforcement learning, particularly through interaction with environments [00:24:40]. This marks the current era as one focused on agents [00:24:56].