Technical RLHF
Expertrewardsignalsforfrontiermodels
High-fidelity reward training data verified by domain experts and the 3+1 consensus protocol before it reaches your corpus.
1Specialist matching by domain and task risk
2Parallel expert evaluation to reduce anchoring bias
3Consensus lock before reward signals enter training
4Audit-ready payloads for model and dataset teams
Methodology
Reward data with less noise.
Traditional RLHF inherits noise from unverified crowd work. Ployos routes tasks to senior specialists, runs parallel evaluation, and requires human consensus plus Shadow AI validation before data ships.
3+1
Consensus
Three senior experts and one autonomous verification pass must agree.
0
Noise Tolerance
Disputed payloads are routed back into review instead of shipped.
1%
Expert Routing
Silent Match selects top-fit reviewers for each technical domain.
72h
Pilot Launch
Start with a scoped reward-data pilot before a full pipeline rollout.