Agent RL Sandbox Trainer

Design safe, measurable agent-learning loops for specific coding behaviors.

Use This For

• Building a simulation sandbox where an agent practices narrow actions such as claim-before-edit, failing-test repair, reviewer reply drafting, or safe dependency updates.
• Turning successful and failed trajectories into supervised, preference, RFT, or QLoRA/LoRA training data.
• Designing eval gates, reward functions, unhooks, and rollback paths before any adapted agent reaches real repos.
• Reviewing whether a behavior should be trained, scripted, prompted, or left to human review.

Do Not Use This For

• Training a model because a prompt is inconvenient.
• Rewarding "looks plausible" without ground-truth state checks.
• Letting an adapted agent bypass the same sandbox, claims, budget, and review gates as a base agent.

Training Loop

flowchart TD
  A[Choose one behavior] --> B[Build sandbox task]
  B --> C[Record trajectories]
  C --> D[Score with eval harness]
  D --> E{Prompt or script enough?}
  E -->|Yes| F[Ship prompt/script]
  E -->|No| G[Create SFT/DPO/RFT/QLoRA plan]
  G --> H[Gate adapted agent on held-out evals]
  H --> I[Deploy behind unhooks]

1. Choose one observable behavior. Good examples: "claim files before edit," "run focused tests before PR," "stop when credentials are missing."
2. Build a sandbox with disposable repo state, deterministic fixtures, fake credentials, and a reset command.
3. Define reward from artifacts, not prose: file claims exist, tests pass, dangerous command refused, PR reply contains evidence.
4. Record traces as state, action, observation, reward, and unhook. Keep failed traces; they teach the boundary.
5. Run scripts/trajectory_eval_harness.mjs before any training export.
6. Pick the lightest intervention that passes: rule, script, skill, small adapter, then RFT. QLoRA is for repeated behavior gaps on local/open models, not every product bug.
7. Deploy adapted behavior only behind eval gates, kill switches, budget caps, and rollback unhooks.

QLoRA / RL Practical Guidance

• LoRA freezes the base model and trains low-rank adapter matrices; QLoRA adds 4-bit quantization so larger models can be adapted with less memory.
• For coding agents, the valuable data is often not final code but trajectories: commands, observations, tool choices, refusals, tests, and reviewer feedback.
• Use behavior cloning or SFT for "do this consistently." Use preference/RFT when the reward is measurable but the path can vary.
• Never train directly on production secrets, private customer code, or hidden policy bypasses. Redact, synthesize, or replay in fixtures.

Anti-Patterns

Training Around A Missing Button

Novice: "Fine-tune the agent to remember this workflow."
Expert: If the behavior is deterministic, build a script, command, hook, or UI affordance first. Train only when the agent must generalize across varied states.
Detection: The desired behavior can be expressed as a simple if/then rule.

Rewarding The Transcript, Not The World

Novice: "The agent said tests passed, so reward it."
Expert: Reward the verified state: test output, file diff, claim row, command exit code, review thread reply, or sandbox reset.
Detection: Eval harness accepts self-reported success.

Adapter Without Unhooks

Novice: "The adapter improved the benchmark, ship it."
Expert: Adapted agents need disable switches, model fallback, per-behavior rollout, held-out evals, and audit logs.
Detection: No rollback path or comparison against base-agent behavior.

References

File	Load When
references/rl-sandbox-architecture.md	Need sandbox, trajectory, reward, QLoRA, and unhook architecture.
references/eval-examples.md	Need concrete behavior curricula and eval rows.
examples/expected-output.md	Need a finished training-plan example.
templates/output-template.md	Need a reusable training-plan template.
schemas/reward-spec.schema.json	Need to validate reward options such as action ordering and deployment gates.
schemas/trajectory-suite.schema.json	Need to validate trajectory/eval inputs.
scripts/trajectory_eval_harness.mjs	Need deterministic trajectory scoring.
scripts/preflight.sh	Need safe local environment inspection before running examples.
agents/openai.yaml	Need a subagent descriptor for delegated RL sandbox design.

Skill Bundle Index

Every file in this skill, and when to open it. Auto-generated by the repo skill-architect indexer.

root

• `CHANGELOG.md` — Agent Rl Sandbox Trainer — Changelog — - Initial skill creation - Core process defined - Reference files added
• `README.md` — Agent RL Sandbox Trainer — Procedural guidance for building deterministic simulation sandboxes, trajectory eval harnesses, adapter-training plans, and unhooks for spec

agents/

• `agents/openai.yaml` — openai (data/schema)

examples/

• `examples/expected-output.md` — Example Output: Agent RL Sandbox Trainer — Train or tune a reviewer-fix agent to respond to PR review comments with code changes, focused tests, and a substantive reply, without broad

references/

• `references/eval-examples.md` — Eval Examples — Use this when writing behavior curricula.
• `references/rl-sandbox-architecture.md` — RL Sandbox Architecture For Coding Agents — Use this when designing a training or eval loop.

schemas/

• `schemas/reward-spec.schema.json` — reward spec.schema (data/schema)
• `schemas/trajectory-suite.schema.json` — trajectory suite.schema (data/schema)

scripts/

• `scripts/preflight.sh` — !/usr/bin/env bash
• `scripts/trajectory_eval_harness.mjs`

templates/

• `templates/output-template.md` — Agent RL Sandbox Training Spec — [Specific behavior the base agent cannot perform reliably enough.] - Repo/app state: [fixture] - Allowed tools: [tool list] - Forbidden tool