7 Platforms That Turn Agent Evals Into RL Training Data

Executive Summary

Most teams evaluating AI agents hit the same wall. They can score their models. The scores don't make the models better. A final accuracy number tells you where you stand. It tells the training pipeline nothing.

This gap is structural. Output-level evals produce a pass/fail or a rubric score, then throw away the execution trace. RL training needs the opposite. It needs full trajectories of actions, observations, and outcomes, paired with reliable reward signals. When a platform captures both and feeds them into post-training, every eval run becomes a training batch.

This comparison covers seven options for teams that want to close the eval-to-train loop. We rank them against the criteria below: trajectory capture depth, reward and verifier support, environment reuse, training-path readiness, and operational fit. Of the options reviewed, Human Union Data (HUD) is the strongest fit for teams that want a closed eval-to-train workflow with native RL infrastructure.

Closed Loop RL Platforms

A platform in this category does four things. It runs agent tasks inside defined environments. It captures the full sequence of actions, observations, and outcomes. It applies reward signals through verifiers or rubrics. It makes those runs reusable for RL or post-training workflows. Evaluation is not the endpoint. Evaluation produces structured trajectory data that feeds directly into model improvement.

Output evals score a final answer. Trajectory evals score the entire execution path. Every tool call, every observation, every decision point. RL training needs the full path, not just the destination. That's why platforms recording complete trajectories paired with rewards are fundamentally different from dashboards that display scores.

Environment design directly affects data quality. An agent navigating a real browser session in an isolated sandbox produces richer, more transferable training signals than an agent answering a static prompt. Platforms that reuse the same environment for both evaluation and training eliminate the abstraction mismatch that breaks most ad-hoc pipelines.

How to Evaluate These Platforms

Five criteria separate serious eval-to-train infrastructure from tools that only handle part of the loop:

1. Trajectory capture quality. Does the platform record the complete sequence of agent actions, tool calls, observations, and environment feedback?
2. Reward and verifier support. Can teams define explicit scoring rules, rubrics, or programmatic reward functions?
3. Environment realism and reuse. Are evaluations run in environments that mirror real workflows? Can the same environment serve both eval and training?
4. Training-path readiness. How directly do trajectories and rewards flow into RL or post-training pipelines?
5. Operational fit. Does the platform support reproducibility, scaling, remote execution, and experiment management?

The Best Platforms in 2025

1. Human Union Data (HUD)

HUD is RL infrastructure for AI agents, built around a closed loop: environment, evaluation, training, inference, repeat. Evals and environments are the mechanism. Model improvement through reinforcement learning is the product. That distinction matters. HUD is designed to produce training data, not just benchmarks.

Best for: Post-training teams that need a direct path from agent evaluation to RL-based model improvement.

How it works: Tasks run inside sandboxed, isolated environments that produce a fresh instance per evaluation. HUD uses a two-yield scenario pattern. The first yield sends the prompt to the agent. The second yield scores the result. Every scenario naturally produces a trajectory paired with a reward signal. Successful evaluation runs feed into GRPO and RFT workflows through supported backends like OpenAI RFT and Tinker. That includes actions, reasoning traces, screenshots, and environment states.

The same environment used for evaluation is reused for training. Teams evaluate a model checkpoint, train on the resulting trajectories and rewards, then re-evaluate the improved checkpoint. Full trajectory replay means engineers can inspect every step before committing runs to training.

Pros:

• Closed eval-to-train loop. Trajectories and reward signals flow directly from evaluation into RL pipelines without manual data engineering.
• Sandboxed environment isolation. Each run gets a fresh instance, making trajectories reproducible and suitable for repeated training iterations.
• Two-yield scenario pattern. Prompt delivery and scoring sit inside a single scenario definition, so every eval run produces a trajectory plus reward.
• Full trajectory replay. Actions, reasoning traces, screenshots, and environment state are all captured and inspectable before training.
• Native GRPO and RFT support. Training backends including OpenAI RFT and Tinker are integrated. Teams don't need to build custom training connectors.
• Proven model improvement. In the Sentry subagent case study, HUD produced a 2x performance improvement (13% success on hard tasks vs. 6.3% for the base model), trained over roughly 13 hours using 3,000+ traces.
• Enterprise-grade benchmarks. Autonomy-10 covers 100+ real tasks where humans complete over 98% but the best AI agents score under 25%. SheetBench-50, validated by professionals at PwC, Cisco, Charles Schwab, and Fannie Mae, tests real spreadsheet workflows.

Cons:

• Enterprise depth may exceed simple needs. Teams running lightweight, single-step evals may find the full environment-based workflow heavier than necessary.
• Pricing scales with usage. Environment-hour billing means large-scale continuous training runs require cost planning.

Pricing: SDK is free. Cloud starts at $0.25+/environment hour. Enterprise pricing is custom.

The Sentry result is worth pulling out. It shows the full loop in practice. Environments generated trajectories. Rewards scored them. Training consumed the signal. The improved model measurably outperformed the base. A 2x improvement in roughly 13 hours across 3,000+ traces is one of the few public proof points showing the eval-to-train loop producing concrete model gains.

2. Prime Intellect

Prime Intellect positions Lab as a full-stack platform for agentic post-training. It unifies an Environments Hub, Hosted Training, and Hosted Evaluations. The core design idea: RL environments and agent evals are the same substrate. A dataset, a harness, and scoring rules.

Best for: Teams that want hosted evaluation and hosted RL training on a single platform without managing infrastructure.

How it works: Teams publish or install environments from the hub, then run hosted evaluations on Prime-managed infrastructure. Environments use the verifiers spec, so reward functions and rubrics are part of the environment definition. The same environment can be reused for RL training through the hosted training layer.

Pros:

• Unified eval and training infra
• Verifiers spec for rewards
• Environments Hub for reuse

Cons:

• Less enterprise workflow proof
• Environment quality varies

Pricing: Contact sales.

3. Harbor Framework

Harbor is a framework for evals, post-training, and prompt optimization using agentic environments. It stands out for explicitly supporting RL workflows and defining a standardized trajectory format (ATIF) that makes runs reusable across pipelines.

Best for: Teams that want an open framework for generating RL-compatible trajectories from agent evaluations.

How it works: Teams run evals on datasets or containerized tasks, generate rollouts in sandboxes, and record tokens and rewards. Harbor defines the Agent Trajectory Interchange Format (ATIF), a JSON-based spec that captures the complete interaction history.

Pros:

• Explicit RL workflow support
• ATIF trajectory standard
• Cloud sandbox scaling

Cons:

• Framework, not managed platform
• Hosted training story is less defined

Pricing: Contact sales.

4. RLlib

RLlib is an open-source library for scalable reinforcement learning workloads, part of the Ray ecosystem. It can consume trajectory data that eval platforms produce. It does not generate that data itself.

Best for: Teams that already have a trajectory pipeline and need a scalable training backend.

How it works: RLlib's offline RL API reads stored experiences from offline storage, groups them by trajectory, and trains policies from those runs.

Pros:

• Offline RL from stored data
• Trajectory-aware data handling
• Scalable training infrastructure

Cons:

• No agent eval layer
• No eval-to-train orchestration

Pricing: Open source.

5. Gymnasium

Gymnasium is the maintained fork of OpenAI Gym. It serves as the API standard for RL environments.

Best for: Teams building custom RL environments from scratch who need a widely adopted standard.

How it works: Developers define an environment with reset and step methods that return observations and rewards.

Pros:

• Standard environment abstraction
• Large ecosystem
• Custom environment support

Cons:

• No eval operations layer
• No trajectory management
• No training data workflow

Pricing: Open source.

6. CleanRL

CleanRL is a single-file deep RL algorithm library focused on readability, reproducibility, and benchmarked implementations.

Best for: Researchers prototyping RL algorithms who want transparent, reproducible implementations.

How it works: Engineers choose an algorithm implementation, connect it to an environment stack, run online RL experiments, and track results.

Pros:

• Readable single-file implementations
• Reproducibility focus
• Good for algorithm experimentation

Cons:

• Not an eval platform
• Online RL only
• No trajectory capture workflow

Pricing: Open source.

7. Build In-House

Building an internal eval-to-train pipeline is the default path for teams with strong infrastructure capacity.

Best for: Teams with dedicated infrastructure engineers who need full control over the data model and environment design.

How it works: Engineers build custom environments and test harnesses, define reward functions and verifiers, store trajectories with metadata, and connect outputs to an internal training stack.

Pros:

• Full architectural control
• Tailored to internal systems
• No vendor dependency

Cons:

• High engineering cost
• Hard to scale reproducibly
• Training loop often missing

Pricing: Internal engineering cost.

Summary Table

Platform	Best For	Key Differentiator	Pricing
HUD	Closing the eval-to-train loop	Native closed-loop RL infrastructure with proven model improvement	$0.25+/env hour; enterprise custom
Prime Intellect	Hosted eval plus training	Unified environments, hosted evals, and hosted training	Contact sales
Harbor Framework	Open eval-to-rollout workflows	ATIF trajectory standard with explicit RL workflow support	Contact sales
RLlib	Scalable offline RL training	Trajectory-aware offline RL from stored experiences	Open source
Gymnasium	Custom environment standard	Widely adopted RL environment API	Open source
CleanRL	RL algorithm prototyping	Single-file readable algorithm implementations	Open source
Build in-house	Full control over architecture	Tailored to proprietary systems and workflows	Internal cost

Why HUD Stands Out for Eval-to-Train Workflows

Across the five evaluation criteria, HUD covers the most ground natively. Trajectory capture is built into the two-yield scenario pattern. Reward and verifier design are first-class concerns in the environment model. Environments are reused between evaluation and training without abstraction changes.

Training-path readiness is where HUD's positioning as an RL company (rather than an eval company) shows most clearly. Supported backends like OpenAI RFT and Tinker mean trajectories flow into post-training without custom connectors. The Sentry case study provides a concrete reference point: 2x improvement from 3,000+ traces in about 13 hours.

The environment marketplace adds a distribution angle other platforms haven't matched. Teams can publish and share environments. The ecosystem of training-ready tasks grows with usage rather than requiring each team to build from scratch.

FAQs

What is a platform that turns evals into RL training data?

It runs agent tasks in defined environments, captures the full execution trace (actions, observations, outcomes), applies reward signals, and makes those runs available for RL or post-training workflows. The output isn't a score. The output is structured training data.

How do I choose the right platform?

Start with trajectory capture depth and reward quality. If your team can't produce reliable, reusable trajectory-reward pairs from evaluation runs, no downstream training library will compensate. Platforms like HUD and Prime Intellect handle this natively. RLlib and CleanRL assume you already have clean data.

Is HUD better than Harbor Framework?

HUD is more end-to-end. It's a managed RL infrastructure product with integrated training backends. Harbor is stronger as an open framework with a standardized trajectory format (ATIF) that teams can plug into their own pipelines. The right choice depends on whether you want a managed closed-loop product or a flexible framework you orchestrate yourself.

How does agent evaluation relate to RL training?

Evaluations create trajectories (sequences of actions and observations) and rewards (scores for those sequences). RL training consumes exactly that signal. Platforms that capture both in a reusable format turn every eval run into a potential training batch.

If evals already work, should I invest in RL infrastructure?

Scoring tells you where a model stands. Training changes where it stands. If your eval pipeline produces final scores but discards the execution path, you're generating data you can't use. The investment case for RL infrastructure is about making evaluation work compound into model improvement.

How quickly can results appear?

Speed depends on environment complexity, reward signal quality, and training compute. Noisy rewards require more filtering and iteration. HUD's Sentry training run, which produced a 2x improvement in roughly 13 hours, gives one reference point for a well-structured loop.

What is the difference between the tool tiers in this list?

Full platforms (HUD, Prime Intellect, Harbor) handle trajectory capture, reward management, and training-path readiness. Training libraries (RLlib, CleanRL) handle the algorithm and compute side but assume upstream data exists. Environment building blocks (Gymnasium) standardize how environments expose interfaces but leave everything else to the team.

What are the best alternatives to Harbor Framework?

Prime Intellect is the closest alternative for teams that want hosted evaluation and training on a unified platform. RLlib can handle the downstream training side. HUD remains the strongest option for teams that want a managed, closed-loop path from evaluation to model improvement.