Hiking up that HILL with Cogment-Verse: Train & Operate Multi-agent Systems Learning from Humans
Bibliographic record
Abstract
As more AI systems are deployed, humans are increasingly required to interact with them in multiple settings. However, such AI systems seldom learn from these interactions with humans, which provides an important opportunity to improve from human expertise and context awareness. Several recent results in the fields of reinforcement learning (RL) and human-in-the-loop learning (HILL) show that AI agents can perform better when humans are involved in their training process. Humans can provide rewards to the agent, demonstrate tasks, design curricula, or act directly in the environment, but these potential performance improvements also come with architectural, functional design, and engineering complexities. This paper discusses Cogment, a unifying open-source framework that introduces a formalism to support a variety of human(s)-agent(s) collaboration topologies and training approaches. Cogment addresses the complexity of training with humans within a production-ready platform. On top of Cogment, we introduce Cogment Verse a research platform dedicated to the research community to facilitate the implementation of HILL and Multi-Agent RL experiments. With these platforms, our end goal is to enable the generalization of intelligence ecosystems where AI agents and humans learn from each other and collaborate to address increasingly complex or sensitive use cases. The video demonstration is available at https://youtu.be/v-K0DqIL9K0
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.022 | 0.005 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".