Memory Stream: Enhancing Information Flow in Recurrent Memory Transformers for Efficient Long-Context Training
Why this work is in the frame
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Bibliographic record
Abstract
Abstract A fundamental limitation of Transformer-based models is their quadratic computational complexity with respect to input length, which limits their applicability to long-context tasks. Recurrent Memory Transformer (RMT) addresses this by introducing a memory mechanism that enables segment-wise recurrent processing. However, RMT relies on a multi-stage training curriculum that increases computational costs and complexity during fine-tuning. In this work, we propose the Recurrent Memory Transformer with a Memory Stream (RMT-MS), a novel architecture with layer-wise memory states and horizontal memory connections across segments. These mechanisms increase memory capacity and improve information flow, reducing the need for curriculum learning. We evaluate RMT-MS alongside RMT and ARMT on three long-context tasks: associative retrieval, BABILong QA1, and QA3. Our experiments show that RMT-MS achieves strong performance in single-stage training, matching curriculum-trained baselines on simpler tasks, and narrowing the gap on more complex ones. These results highlight the potential of RMT-MS for efficient long-context modeling without costly training schedules.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it