As process-based reward modeling becomes table stakes, the gap isn't in compute—it's in access to reasoning traces that show how humans actually think under uncertainty. Most RLHF data is static preferences. You need causal chains, multi-step reasoning, value-level structure, and belief dynamics across time. LifeTrade is built for that. We're working with leading labs who are moving to process supervision, and we're designed to flex with your pipeline, not force you into ours. Pilot design, custom cohorts, rolling data delivery, ongoing alignment work—whatever lets your team move fastest. If you're training process-based reward models or value alignment systems, let's talk about what a partnership looks like.
Most RLHF data captures preferences—A is better than B—but not the reasoning structure underneath. Process-based reward models need something different: multi-step decision traces, explicit counterfactuals, and value-level structure. You can't see how judgment shifts with context, what would flip a choice, or which tradeoffs a person is weighing. That's not a volume problem. It's a design problem.
Same people, repeated sessions over weeks. Reveals how reasoning evolves with changing context and stakes—not snapshots, but trajectories. You see how values actually form and shift.
Every decision captures explicit "because" reasoning, flip conditions, and counterfactuals—what people weigh, what would flip them, why they choose. Structure, not just outcomes.
Real contexts, real stakes—commuting, between obligations, navigating actual constraints. Models trained on reasoning as it unfolds in the world, not synthetic prompts.
Multi-step reasoning traces with explicit intermediate states—exactly what you need to train reward models that evaluate reasoning steps, not just final answers.
Embedded tradeoffs made explicit (autonomy vs. safety, family vs. career, speed vs. thoroughness). You can audit which human values are being encoded in your models.
When you're generating preferences at scale, this data grounds synthetic outputs in real human judgment—keeping your feedback loops honest.
Built-in "what would flip my choice" annotations in every trace. Your models can learn causal structure and decision boundaries, not just correlation patterns.
We start by understanding your specific problem—what your reward model needs to learn. What we figure out together: Target cohorts: Who makes the decisions your model needs to understand? (Parents navigating healthcare tradeoffs? Mid-career professionals weighing job changes?) Decision domains: What types of reasoning matter most? (Risk tolerance, time preferences, ethical boundaries?)Data formats: What does your pipeline expect? (Preference pairs for DPO? Chain-of-thought traces? Flip conditions?)
Timeline: Usually 1-2 calls. We move fast.
Once scoped, we recruit verified participants and start rolling interviews. Same people, multiple sessions over 2-4 weeks—so you see how reasoning evolves.
Data arrives schema-aligned and ready to integrate into your stacks.
What "plug-and-play" actually means:
Integration support: If your pipeline needs adjustments, we adjust. Not a "take it or leave it" data dump.
Once the pilot works, you choose how to scale.
Ongoing streams:
Continuous data delivery for models that need to stay aligned as the world changes. New cohorts, new domains, new reasoning patterns—captured and structured on a rolling basis.
Bespoke cohorts:
One-time deep dives into specific populations or decision types. (Example: "We need 500 episodes from parents navigating school choice decisions under financial constraints.")
Licensing:
Pricing: Per-episode or per-cohort. Transparent. No surprises.
Most RLHF data comes from anonymous crowdworkers doing one-off A/B ratings on screens, which captures preferences but not the underlying decision process.LifeTrade is built from first principles around longitudinal, in-situ conversations with verified participants, giving you causal reasoning traces, belief updates, and cross-session dynamics that commodity annotation platforms cannot provide.
Each LifeTrade conversation is converted into structured decision episodes with context, options, chosen action, explicit "because" reasoning, flip conditions, and emotional state—formatted as JSONL preference pairs and trajectory schemas used by leading labs for Reinforcement Learning from Human Feedback (RLHF) and Direct Preference Optimization (DPO).
You receive three data layers in every dataset:
This means you can plug LifeTrade data directly into your existing post-training workflows without building custom tooling around raw audio or unstructured interviews.
LEAF (Lived Experience-centered AI) is a research framework from Georgia Tech that argues AI systems trained only on abstracted behavior fail in the real world because they miss how people actually think through decisions in context. LifeTrade's methodology is designed to capture the kind of lived-experience signals LEAF describes—first-person, contextual, embodied reasoning—in a format that training teams can actually use.
A standard 15–20 minute session yields multiple structured decision episodes, each with explicit causal reasoning and flip conditions, which can be expanded into several preference pairs or trajectory segments. Over a multi-session cohort, this compounds into dense longitudinal traces for each participant rather than isolated datapoints.
Typical pilots run over 2–4 weeks, from cohort setup and interview design to first delivery of structured datasets ready for your internal evaluation and training workflows. The goal is to validate end-to-end: data quality, integration friction, and measurable impact on your target metrics.
LifeTrade is designed for frontier labs and enterprises building agentic systems that must make high-stakes, human-aligned decisions; early partners are drawn from that group. Pilot and partner details can be shared under NDA as part of a deeper technical conversation.
Participants are personally onboarded, identity-verified, and authenticated by phone before each session, creating a reusable longitudinal panel with strong provenance guarantees. All conversations are encrypted and anonymized before delivery; labs access reasoning patterns and metadata, not personally identifying information.