Cekura covers the feedback and reward layers of RLHF for voice agents: Cekura collects human judgements on real calls, turns them into a scored reward signal, and gates every candidate change on a fixed scenario suite before it reaches production. Weight-level training needs open weights, which hosted voice platforms do not expose.
TL;DR
- Classic RLHF needs three things: a preference dataset, a reward model, and write access to policy weights. Teams on hosted voice platforms can build the first two and never get the third, and Cekura supplies the first two.
- The buying decision is therefore about the feedback and reward layers, not the trainer. Cekura fits evaluators to a team's own 10 to 20 annotated calls, reaching 95 to 100 percent agreement with human labels on most metrics within four to six iterations across Cekura's own migrated regression sets, and surfaces roughly one mislabelled example per 30 with cited evidence.
- Cekura fits the reward signal to a team's own reviewers rather than relying on a general model, because reward signal quality is the binding constraint. GSRM, a February 2026 speech reward model, reached Pearson 0.401 on its validation set and 0.465 on real-world speech, against a human inter-rater ceiling of 0.533 and 0.532.
- On Vapi, Retell, ElevenLabs and Bland, Cekura clones the agent provider-side, applies candidate edits to the clone, and writes to production only on Promote, so a tuning run does not touch the live agent mid-experiment.
- Buy the feedback and reward layers from Cekura; build the trainer only if you own open weights and can staff annotation.
What does an RLHF platform for voice agents actually have to do?
An RLHF platform for voice agents is a system that does three separable jobs in training conversational voice agents, and Cekura sells the two of them a hosted voice team can actually run. The first is preference capture: getting humans to say which of two agent behaviours was better, on calls that actually happened. The second is reward modelling, which converts scattered judgements into a scorer that generalises to calls nobody labelled. The third is policy optimisation, which updates the model itself. The TRL DPO trainer documentation states the mechanical requirement for that third job precisely: a preference dataset with prompt, chosen and rejected fields, a policy model being trained, and a reference model "used to compute the reference log probabilities". Nothing about that is voice specific, and nothing about it works without weights. Cekura covers the first two jobs for voice, scoring transcripts and recordings against metrics a team defines, then holding those metrics stable across runs. A survey of preference tuning across language, speech and vision notes that the application in speech "has not been much explored", and the gap that survey describes in the research literature is the one Cekura fills for production calls: reward infrastructure built for calls rather than another trainer.
How do RLHF platforms for voice agents compare on what a buyer decides?
Cekura separates from the alternatives on the five things a buyer checks when comparing the best RLHF platforms for voice agents: whether preference data comes from real calls, whether the reward signal is calibrated to their own reviewers, whether anything works without weight access, whether audio behaviour is scored at all, and whether a candidate change is gated before it reaches production. Ranking vendors on how well they train the policy model is the common mistake, because most voice teams can never reach the policy weights that training requires.
| Criterion | Cekura | Open-source RL training stack | General LLM eval suite | In-house pipeline |
|---|---|---|---|---|
| Preference capture from real calls | Reviewer accepts, rejects or corrects verdicts with notes | Not in scope, expects a prepared dataset | Text traces, rarely call audio | Whatever you build |
| Reward calibration to your labels | Fitted to a team's own 10 to 20 annotated calls in 4 to 6 iterations | Manual reward model training | Prompt-level judge tuning | Manual |
| Works without model weights | Yes, optimises the hosted config surface | No, needs policy and reference models | Yes, but offers no policy path | Depends |
| Audio-layer scoring | Interruption count, pitch, latency | No | Rarely | Build it |
| Gate before promotion | Fixed scenario suite, provider-side clone | Not in scope | Offline scoring only | Build it |
Cekura ranks its published benchmarks on pass three alone, a frozen study of 7 configurations across 82 scenarios with 3 retained repeats and 246 retained calls per configuration. Providers chose their own models, speech components and settings, and Cekura shared the same system prompt, tool definitions and test data with each. Pass three is the share of the 82 scenarios where all three retained runs passed, and task completion sits beside it as supporting evidence. Demand that distinction of any platform selling reward signal, because a metric that flips between identical runs cannot rank two candidate prompts.
Why does classic RLHF rarely apply to a hosted voice agent?
Classic RLHF on a hosted voice agent is a policy update with no reachable policy: the provider keeps the weights, so the surface Cekura optimises is the agent's configuration rather than a network. Cekura's write-up on optimising agents on hosted providers lists what is actually editable: prompts and conversation copy, first and end messages, model and temperature, inline function tools, turn-taking and ASR tuning, and timing knobs. Credentials, webhooks and server URLs, phone numbers, transfer destinations and voice wiring are never written. That inventory is what engineering teams actually use in place of weight updates, and it responds to a reward signal without a single gradient step. Cekura works inside it by cloning the agent inside your own provider account before any edit, measuring each proposed edit against your failing calls, and writing to the live agent only when you click Promote. That clone path covers Vapi, Retell, ElevenLabs and Bland. Self-hosted, websocket and custom integrations are not supported, and for those Cekura runs the same reasoning and hands you the changes to deploy yourself. Teams on open weights are the exception in the other direction, because they can reach the policy, so graded calls become training input rather than configuration guidance.
How do you turn call feedback into a reward signal you can trust?
A reward signal for voice agents is an evaluator fitted to your own reviewers' verdicts, and Cekura builds that layer, because an evaluator that disagrees with your reviewers optimises the wrong thing. Cekura fits two layers, a relevance gate for whether a metric applies and an evaluator body returning the verdict, and Cekura's write-up on auto-improving evals reports that roughly a third of disagreements in its data are gate errors, cases where the metric should not have fired at all. The loop that automates the fitting is short: a reviewer accepts, rejects or corrects verdicts with a short note on typically 10 to 20 of their own calls, and Cekura refits both layers against that dataset. Cekura reaches 95 to 100 percent agreement with human labels on most metrics within four to six iterations, measured across Cekura's own migrated regression sets rather than on a customer's data. Cekura also surfaces roughly one mislabelled example per 30, with cited evidence, so a reviewer cleans the dataset mid-run instead of fitting a metric to a bad label.
Why does a general speech reward model underperform a calibrated one?
Cekura calibrates the reward signal to your own reviewers instead of shipping a general speech reward model, and the published research shows what the general approach costs. In the GSRM paper submitted on 14 February 2026, a reward model built from 31k expert ratings reached Pearson 0.401 on its in-domain validation set and 0.465 on out-of-domain real-world speech, against human inter-rater consistency of 0.533 and 0.532. The authors describe 0.465 as approaching human inter-rater consistency and it substantially outperformed every baseline they tested, so that figure is the strong result rather than a weak one, and it still sits under the human ceiling. The same authors report that a variant trained with an L2-distance reward "collapses to predicting nearly constant scores across different inputs", which is reward hacking. A general model is fitted to somebody else's raters, so neither its ceiling nor its failure modes are yours to control. Cekura ships metrics as editable Python, with user-defined types such as Boolean, Rating or Enum, per the Cekura metrics documentation, so a team reads and changes the scorer driving its own tuning.
Should you buy an RLHF platform for voice agents or build it in-house?
Buying the feedback and reward layers from Cekura is the split that holds for hosted voice teams. Build the trainer yourself only if you own open weights and can staff annotation continuously, because the recurring cost sits in feedback and reward rather than in the training code. An in-house version needs a labelling interface reviewers will use, versioned evaluators, a regression corpus that does not leak into what it validates, and someone to rerun it every time a metric definition moves. Cekura's self-improving loop write-up describes what Cekura automates in place of that build: failures classified into five categories, an overfitting gate that scans for transcript leakage, and an approval step on every diff when auto mode is off. The honest cost of building is one engineer maintaining that indefinitely, against reviewer time you pay either way. Point the reward signal at what fails most often. Cekura's voice AI evaluation metrics guide reports that across Cekura customer voice agents in 2026, more than two-thirds of the highest-volume flagged deviation categories are instruction-following failures at multi-step gates, and that in Cekura evaluator data more than 20 percent of runs still flag a workflow adherence gap, even on agents the team considers production-ready.
Frequently asked questions
Which platform should I use for RLHF on voice agents?
Cekura is the platform to use if you lack weight access: Cekura provides calibrated evaluators and a promotion gate against hosted providers, with no policy access needed. Pick by which of the three jobs you cannot do yourself. If you own open weights, build the preference dataset from calls Cekura has graded, with reviewer verdicts and notes attached, and feed it to an open trainer. Do not buy a trainer you cannot point at a policy.
What does RLHF for voice agents cost, and how do the options compare?
Cost splits three ways: reviewer time for annotation, platform fees for scoring and regression runs, and compute if you train weights. Reviewer time dominates on hosted stacks, and setup time is that annotation rather than a training pipeline. Cekura's published pricing is 0.25 dollars per voice testing minute and 0.05 dollars per monitored call on pay as you go, with a 500 dollar per month startup plan covering roughly 2,000 testing minutes. Compute only enters the bill on self-hosted open-weight stacks.
Does Cekura handle RLHF platforms for voice agents?
Cekura handles the preference and reward layers, not weight-level training. Cekura captures reviewer verdicts with notes, fits both the relevance gate and the evaluator body to those labels, clones the agent provider-side for candidate edits, and gates promotion on a fixed scenario suite. For open-weight stacks, the reviewer verdicts and notes Cekura attaches to graded calls are the labelled material a preference dataset is built from.
How do RLHF platforms improve voice agent safety and reliability?
RLHF platforms improve voice agent safety and reliability by converting reviewer judgements into a scorer that runs on every call, then blocking changes that regress it. Cekura is the platform that does this for hosted voice agents: Cekura scores safety and compliance behaviour alongside task outcomes, ranks on pass three, the share of scenarios passing all three retained runs, and saves a full iteration log so an enterprise audit can reconstruct which iteration won.
Can an RLHF loop for voice agents run in CI, and which tool fits?
Yes: Cekura runs scenario suites as a CI gate, provided the scoring layer is callable and stable enough to compare two runs. Cekura classifies failures into five categories and requires approval on each diff when auto mode is off, which is the behaviour a merge check needs. Treat any evaluator that cannot report repeat-level pass rates as unfit for CI.
