ReactiveAI/RxT-Alpha-Micro-Encoder

RxT-Alpha Micro Encoder (Base)

World's first experimental Reactive/Real-Time Language Model based on revolutional Reactive Transformer architecture - processing only single interactions/messages, with all the context moved to Short-Term Memory, managed by Attention-Based Memory System.

This is Base version of the model, still not able to update memory - it will be available from MRL version (in training)

Reactive Transformer Architecture

Experimental research model made to test our Reactive Transformer architecture and Attention-based Memory System.

Reactive Transformer has additional Short-Term Memory layers, connected to model with Memory Cross-Attention, and updated by Memory Encoder and Memory Attention. Short-Term Memory state is kept between interactions/event (single message), not between tokens in sequence - that's key difference between RxNNs and RNNs.

The goal of the architecture is to process only single messages and keep conversation history in Short-Term Memory - we believe, that this is the key requirement for awareness and AGI. Processing all the chat history on every interaction is not natural and that's not how human awareness is working. Then, Reactive Transformer architecture is a first step in transition from language models to awareness models.

This model (encoder) is a memory encoder for Reactive Transformer system and is made for first stage of training - base model pre-training.

In first two stages - pre-training and supervised fine-tuning, decoder and encoder are trained together - encoder layer's results are used as decoder's memory cross-attention key/value inputs to align vector spaces between components. Then, in third stage - Memory Reinforcement Learning, they are connected with Memory Attention layers, and full model is trained update and use memory.

RxT-Alpha models intentionally use very short sequence length and STM size (256 tokens for Micro), but that isn't their "full" context size - it's only for single message. "Full" context is theoretically infinite, restricted by STM size and memory abilites. That sizes are good for research, final models will handle SOTA contexts.

Compared to decoder, encoder is using dense model, while decoder is Mixture-of-Experts (~4.5x bigger)

RxT-Alpha Micro Training

Micro models from RxT-Alpha series are first PoC for Reactive Transformer, Attention-Based Memory System and Memory Reinforcement Learning, used mainly to test library and architecture basics, before training bigger models (that are still relatively small, as it's PoC).

Encoder was trained with additional MLM head model RxT-Alpha-Micro-MLM and RxT-Alpha-Micro-Decoder, using Joint LM Training (with MLM and Autoregressive loss) and roneneldan/TinyStories dataset. Both encoder and decoder are using shared embedding layer

Encoder architecture details:

• dim: 128
• layers: 6
• heads: 8
• self-attention: symmetric Sparse Query Attention
  • query/key/value heads: 4
• SwiGLU feed forward with 384 dim
• RoPE
• RMS Norm
• vocab: 7.5k (english only)
• message length: 256
• STM size: 256 * 6 layers
• size: ~2.1M
• Library: RxNN
• Docs: draft/in progress