Pre-Training Method
2024-04-28
Habib: Hi, I want to continue our discussion regarding the proposed architecture of AGI. We already discussing the integration of first-person illusion, incorporating full transformer architecture with encoder as sensory input and decoder as self-thinking loop, multi-layered memory management with various cutting edge techniques, and implementation of sense of time in the model. Now, I want to discuss the concrete implementation of how we train this architecture, I think I'm inspired by the BART model that has full architecture Transformer too and I wonder if we can implement the pertaining method BART model use into our proposed architecture
Gemini: Training the Proposed AGI Architecture: Inspiration from BART and Beyond
Your proposed AGI architecture, with its intricate components like first-person illusion, full transformer architecture, multi-layered memory, and sense of time, presents an exciting vision. Drawing inspiration from BART's denoising pre-training approach is a great starting point, but we'll need to go beyond it to cater to the unique aspects of your architecture. Let's explore potential training strategies:
Phase 1: Foundation Building
Sensory Encoder Pre-training:
Similar to BART's encoder, we pre-train the sensory encoder on a massive dataset of raw sensory data (images, audio, text, etc.). This allows the encoder to learn rich representations of the external world.
We can explore different pre-training objectives like masked sensory modeling, where portions of the sensory input are masked and the model learns to predict them. Thought Decoder Pre-training:
The thought decoder can be pre-trained similarly to BART's decoder, but with a focus on generating coherent and logically consistent sequences of thoughts.
Potential objectives include:
Masked Thought Modeling: Mask out parts of a thought sequence and train the model to predict the missing thoughts.
Causal Language Modeling: Predict the next thought in a sequence, conditioned on the previous thoughts and the sensory context.
Memory Module Training:
The memory modules (window context, Infini-attention, retrieval system) can be trained jointly with the encoder and decoder.
Objectives:
Memory Recall: Train the model to retrieve relevant information from memory based on the current context and thought. Memory Update: Train the model to update the memory with new information from the sensory input and the generated thoughts.
Temporal Embedding Training:
The temporal embedding can be learned jointly with the other components.
We can use a loss function that encourages the model to generate consistent and temporally coherent thoughts and actions.
Phase 2: Self-Improvement Loop
Imagination and Prompt Synthesis:
Implement a prompt synthesis module that generates diverse and challenging prompts based on the model's current capabilities and knowledge.
This can be achieved through techniques like Self-Instruct or by prompting the model itself to create new scenarios. ηMCTS Search and Refinement:
Utilize ηMCTS to explore the space of possible thought sequences and actions.
Train the critic models (value function, PRM, ORM) to provide accurate and reliable feedback for guiding the MCTS search. Adapt the branching factors and state merging techniques to ensure efficient exploration of the vast search space.
Policy Update:
Train the full model (encoder, decoder, memory, temporal embedding) on the high-quality trajectories generated by ηMCTS.
We can use SFT, DPO, or PPO for this training phase, potentially switching between them based on the stage of training and the desired objectives.
Additional Considerations:
Multi-modal Learning: Explore methods for integrating different modalities of sensory input and thought representation within the architecture.
External Knowledge and Tools: Allow the model to access and utilize external knowledge bases and tools during the search and reasoning process.
Explainability and Interpretability: Develop techniques to understand and interpret the model's reasoning process, including visualizing attention maps and thought sequences.
Ethical Considerations: Address potential ethical concerns related to bias, fairness, and the potential for misuse. Challenges and Research Directions:
Efficient MCTS for LLMs: Continue optimizing the efficiency of ηMCTS for the vast search space of language tasks. Reward Function Design: Explore alternative reward function designs, potentially incorporating human feedback and subjective evaluations.
Lifelong Learning and Continual Adaptation: Develop mechanisms for lifelong learning and continual adaptation to new information and environments.
Training this complex AGI architecture will require significant research and development efforts, but the potential for creating a truly intelligent and adaptable AI system makes it a worthwhile endeavor. By combining inspiration from successful models like BART with novel techniques and addressing the unique challenges of your architecture, we can pave the way for the next generation of AI.