In a landscape where training large language models (LLMs) with Reinforcement Learning from Human Feedback (RLHF) is the new gold standard, one brutal truth remains: RLHF is expensive.
At Blomega, we knew we needed to break the cycle. Our clients demanded scale, speed, and quality — but without runaway costs. So we built the Blolabel platform and architected our RLHF pipeline from the ground up with one mission:
Reduce the cost of RLHF by at least 40%, without sacrificing agreement quality.
Here’s how we did it.
1. We Engineered a Performance-Tuned Task Assignment System
Instead of basic equal distribution, we built a smart assignment engine into Blolabel. It routes tasks based on annotator performance (accuracy, pass rate, and consistency) and current workload. This allowed us to:
- Reduce retries and disagreements by 23%
- Increase average annotator throughput by 35%
- Lower per-task overhead without increasing error rate
Bottom line: better talent utilization = fewer reviews, faster convergence.
2. We Integrated Model Confidence Scoring Upfront
Using model-generated confidence scores, we filtered out high-confidence completions that required only light audits. Only low-confidence or edge-case outputs were sent for full HITL review. This:
- Eliminated unnecessary human evaluation on 30–50% of tasks
- Preserved human effort for where it really mattered
Impact: Our clients saw 2x throughput for the same headcount.
3. We Trained and Tiered Annotators Like Athletes
Not all human feedback is equal. So we:
- Developed calibration tests for task onboarding
- Tiered annotators into performance bands
- Assigned tasks dynamically based on their accuracy & agreement scores
High performers got more volume and bonuses. Low performers were retrained or filtered out. This produced:
- A 90%+ agreement rate across the top tier
- Lower review and adjudication cost
4. We Automated Meta-Evaluation and Disagreement Analysis
Blolabel logs every disagreement and learns from them. We:
- Flag edge-case prompts and escalate only those
- Automatically detect spammy or lazy responses
- Created workflows where model + reviewer jointly adjudicate disagreements
Result: Our quality assurance cost dropped by 28%.
5. We Localized Where It Made Sense — But Didn’t Compromise
We smartly balanced global coverage with skill. For multilingual RLHF:
- We used in-market experts for high-stakes domains (e.g., legal, medical)
- But routed general content to mid-cost regions with high accuracy
This blended model saved up to 50% per task in high-volume regions without quality trade-offs.
The Results
Before integrating Blolabel, RLHF operations were costly and inconsistent. After implementation, we saw measurable improvements across key metrics:
- Average Cost per Annotated Pair dropped from $1.80 to $1.05
- Human Agreement Rate rose from 87% to 91%
- Annotator Throughput increased from 220 to 305 tasks/day
- Review Rejection Rate fell from 9.2% to 4.8%
These gains weren't incremental — they were transformative. By combining smart task routing, confident model filtering, and tiered human performance, we redefined what scalable, efficient RLHF can look like.
We didn’t just reduce cost. We created a new RLHF ops model that scales.
If You’re Building the Next Generation of Aligned Models…
You need feedback loops that scale with precision. Not bloated operations.
Blomega + Blolabel is your partner for RLHF, QA, and evaluation workflows that move as fast as your models do.
Ready to reduce cost without compromise? Let’s talk.
support@blolabel.ai
