Pareto: Fair Congestion Control With Online Reinforcement Learning
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Modern-day computer networks are highly diverse and dynamic, calling for fair and adaptive network congestion control algorithms with the objective of achieving the best possible throughput, latency, and inter-flow fairness. Yet, prevailing congestion control algorithms, such as hand-tuned heuristics or those fueled by deep reinforcement learning agents, may struggle to perform well on multiple diverse networks. Besides, many algorithms are unable to adapt to time-varying real-world networking environments; and some algorithms mistakenly overlooked the need of explicitly taking inter-flow fairness into account, and just measured it as an afterthought. In this paper, we propose a new staged training process to train <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Pareto</i> , a new congestion control algorithm that generalizes well to a wide variety of environments. Different from existing congestion control algorithms running reinforcement learning agents, Pareto is trained for fairness using the first multi-agent reinforcement learning framework that is communication-free. Pareto continues training online adapting to newly observed environments in the real-world. Our extensive array of experiments shows that Pareto (i) performs well in a wide variety of environments, (ii) offers the best fairness when it comes to competing with other flows sharing the same network link, and (iii) improves its performance with online learning to surpass the state-of-the-art.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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