Why 150 or fewer concurrent reviewers keeps active learning projects efficient and productive

How many reviewers should you have for an active learning project? The simple answer is: 150 or fewer. It sounds a little dry, but that number sits at the heart of getting meaningful feedback without turning the process into a noisy, tangled mess. In Relativity and similar platforms, active learning thrives when the team communicates clearly, stays organized, and keeps the group size manageable enough to move quickly. Let me unpack why this cap makes sense and how you can make it work in real life.

Active learning in a nutshell

If you’re working with Relativity, you’ve probably seen how active learning uses human judgments to train the system. The idea is simple: label a batch of documents, the system learns from those labels, and then it surfaces the next most informative documents for review. The faster and more reliably reviewers can feed back, the smarter the model gets—without you drowning in a flood of feedback.

But here’s the thing: more voices aren’t always better. You want diverse input, yes, but not chaos. Too many reviewers can slow decision-making, create conflicting signals, and make it harder to reach consensus on tagging, prioritization, and scope. That’s why a ceiling of 150 concurrent reviewers is recommended. It’s big enough to capture variety but small enough to keep conversations focused and decisions timely.

Why 150 feels right (without getting philosophical about it)

• Communication stays crisp: When the group stays under about 150, you can keep the core channels—Relativity workspaces, group chats, and dashboards—clear and responsive. Messages don’t get buried; questions don’t go unanswered for days.

• Coordination remains practical: You can schedule reviews, track progress, and align on labeling rules without a logistics headache. People can participate meaningfully, not just skim a long queue and move on.

• Feedback quality stays high: With a capped group, contributors are more likely to provide thoughtful, specific notes. The learners in the system get better faster because the signal-to-noise ratio stays favorable.

What happens if the number grows beyond the cap?

When you push past 150, you start to see diminishing returns. Threads diverge; you might get conflicting interpretations of the same label or category. Reach for quick, formal escalation processes, but those take time. You’ll also notice more duplication of effort—two reviewers labeling the same document in parallel, each with slightly different reasoning. The net effect is slower iteration and a risk that the active learning loop loses its momentum.

A practical way to structure a reviewer pool

• Roles and responsibilities: Create clear roles—lead reviewers, subject-matter experts, and general reviewers. Each role has specific tasks: reading priority documents, labeling, reviewing model feedback, and validating model-driven selections.

• Curated groups: Instead of one big pool, use multiple, smaller groups that rotate. For example, you might have a core 60-person team plus a rotating supplement of up to 90 people for peak periods. This keeps the system fresh without overwhelming it.

• Defined labeling guidelines: A concise, living set of labeling rules helps keep everyone aligned. When rules drift, the model’s learning gets muddy. Quick reference cards or a shared guidelines document can save countless clarification threads.

• Regular check-ins: Short, focused huddles (weekly or biweekly) help surface ambiguities, recalibrate priorities, and celebrate early wins. You don’t want a long meeting; you want momentum in between sessions.

Using Relativity to keep things smooth

Relativity shines when you leverage its collaboration features and learning loops without letting them spin out of control.

• Workspaces and groups: Organize reviewers into logical groups so that labeling decisions map cleanly to team responsibilities. Groups help you assign batches efficiently and track progress in a central place.

• Flags, tags, and notes: Encourage reviewers to jot quick notes about why a document was labeled in a certain way. Those notes become valuable training data for the model and a reference for others.

• Dashboards and metrics: Keep a pulse on throughput, disagreement rates, and review quality. A dashboard that shows bottlenecks in real time can help you trim the group size or adjust rules before small issues grow into big delays.

• Batch management: Break work into digestible batches. Smaller batches keep the feedback loop tight and reduce the risk of fatigue-based mistakes. If a batch reveals a pattern of confusion, you adjust labeling guidance on the fly.

Handling the inevitable friction

Any large review effort will stumble over occasional tensions—different interpretations, shifting priorities, or unclear instructions. Here’s a pragmatic approach to those frictions:

• Quick escalation paths: When a disagreement blocks progress, have a fast-track process to resolve it. A 24-hour turnaround on decision-making isn’t too much to ask if you want to keep the loop moving.

• Documentation is your friend: Capture the rationale behind labeling decisions in a shared place. It’s not about blame; it’s about creating a learning record for the system and new team members.

• Time-boxed decision windows: Give reviewers specific windows to complete labeling and feedback. A timer creates momentum and helps prevent the process from drifting back to discussion without action.

Onboarding and sustaining a steady rhythm

Bringing new people into an active learning workflow is tricky. You want them productive fast, but you also want to preserve the quality of feedback.

• Lightweight onboarding: A short, practical orientation that covers labeling standards, the reasons behind the active learning approach, and how success is measured goes a long way. A quick “how we work” cheat sheet helps new folks hit the ground running.

• Pairing and mentoring: Pair newcomers with experienced reviewers for a few batches. This accelerates learning and reduces early errors.

• Gentle ramp-downs: If new participants aren’t delivering value after a trial period, reassign them to more focused tasks or rotate them out. It’s better to maintain a lean, lively group than to force square pegs into round holes.

A quick, real-world illustration

Imagine a mid-sized legal matter with a diverse set of documents spanning contracts, emails, and internal memos. The team uses Relativity to run an active learning loop:

• The core reviewer group of 60 handles the most informative batches first, labeling with a crisp, shared rubric.

• A rotating pool of up to 90 additional reviewers steps in for peak loads, focusing on secondary document types and validations.

• The model begins surfacing high-impact documents for review in days, not weeks.

• Regular dashboards show a healthy rate of labeling, a low disagreement rate, and a tight cycle time between labeling and model updates.

• When a labeling ambiguity pops up, the team uses a quick escalation path to decide on a convention and broadcasts the update to everyone. The next batch benefits from that clarified rule, and the cycle continues smoothly.

Key takeaways to carry forward

• Keep the concurrent reviewer count within the 150-or-fewer range. It’s a sweet spot that balances input diversity with the ability to act quickly.

• Structure the team into well-defined groups with rotating participation to preserve energy and focus.

• Build and maintain clean labeling guidelines, and keep the rationale visible. It pays off in faster learning and fewer questions.

• Use Relativity features to organize work, track progress, and surface insights. Dashboards aren’t decoration; they’re decision enablers.

• Plan for onboarding, but also plan for rotation. A steady rhythm beats big, disruptive changes every time.

A few thoughtful reflections

You might wonder if there’s a magic formula that applies to every project. The truth is, no single number fits all situations. Some matters will stay compact with 100 reviewers; others might demand a larger pool during a surge. The 150 cap is a practical guidepost, not a rule carved in stone. The real goal is to keep the active learning loop fast, accurate, and actually usable by the people who are shaping it.

If you’ve ever worked on a multi-person review before, you know the tension between breadth and depth. You want many perspectives to avoid blind spots, yet you also want the group to stay coordinated enough to translate those perspectives into concrete improvements. The cap helps you tip the balance toward productive collaboration, without tipping into chaos.

Final thought

Active learning is a powerful ally when you treat it like a living system—one that benefits from careful calibration, clear roles, and timely feedback. Keeping reviewer numbers within a manageable limit isn’t about restricting voice; it’s about ensuring every voice is heard clearly and has a real impact on the project’s outcomes. When teams blend thoughtful structure with the right tools, the result is a smoother workflow, better quality signals, and faster progress—without the headaches that come with a crowded room.

If you’re drafting plans or refining a current setup, start by auditing your reviewer pool. Are you comfortably under that 150 threshold? Are roles defined and shifted as needed? Do your labeling guidelines feel tight enough to guide decisions without bottling up discussion? Tweak, tune, and then watch the active learning loop do its quiet, steady work. The goal isn’t just faster feedback; it’s smarter decisions built on solid collaboration. And that’s something most teams can feel, almost right away.