Domain-Specific Agentic RL (RFT) for measurable lift

What this is (and why it’s different from “fine-tuning for style”)

Agentic RL: optimize the whole workflow policy (think: tool calls + intermediate decisions + long-horizon strategy), not just response text.
Key pain it addresses: long-horizon credit assignment (good/bad outcomes happen many steps later). The survey highlights approaches like mixing process-level signals with final outcome rewards, and preference optimization over multi-step segments to improve strategic coherence.
OpenAI RFT operationalizes this as: write a numeric grader, upload prompts (+ validation split), run RFT, monitor checkpoints, deploy the best checkpoint—optimized for the metric you actually care about.

When to use RFT (decision rubric)

Use RFT when you have:

Clear success criteria: experts can agree what “good” looks like (low ambiguity).
A gradeable output: you can build a deterministic grader, LLM-based rubric grader, or multi-grader stack.
Headroom: evals show the base system isn’t already at floor/ceiling performance.

Don’t use RFT when:

The task is fundamentally subjective and experts don’t converge (rewrite the task or tighten the rubric).

The “Agentic RL Training Flywheel” (portfolio diagram)

Agentic RL Training Flywheel

Why those optional levers matter:

Process-based supervision improves learning signals in long workflows (denser feedback than end-only rewards).
The survey also describes scaling patterns like automating reward design (via explorer trajectories + reward models) and automated curriculum generation that targets weaknesses to prevent overfitting and accelerate learning.

Implementation blueprint (RFT-ready)

1) Define “policy surface” (what you actually want to optimize)

Tool calls (which tool, when, with what args)
Plan quality (task decomposition, ordering, stopping)
Risk posture (ask clarifying questions vs proceed)
Output format correctness (schemas, constraints)

2) Data: capture trajectories, not just prompts Minimum logging schema:

context snapshot (summaries, retrieved docs, constraints)
action sequence (tool calls + intermediate outputs)
final output (user-visible artifact)
outcome (success label, KPI proxy, human approval)
cost (tokens, tool cost, latency)

3) Graders: stack them (multi-grader strategy) OpenAI supports multiple grader types (string checks, similarity, score-model grader, python execution) and multigraders to combine them.

A practical grader stack for agentic workflows:

Correctness: deterministic checks, schemas, unit tests, invariants
Process quality: rubric scoring (step order, assumption hygiene)
Safety & compliance: policy checks, red flags, PII rules
Efficiency: penalize tool spam, token waste, latency outliers

4) Train and gate like production software

Baseline evals → RFT → checkpoint evals → shadow deploy → canary → full rollout.
Adopt eval-driven iteration (OpenAI explicitly recommends eval-driven development + logging + automation).

Failure modes & mitigations (RFT-specific)

Reward hacking: model optimizes grader loopholes Mitigation: adversarial evals, holdout graders, random audits, multi-grader redundancy.
Over-optimization / narrow generalization: wins on training rubric, loses in real traffic Mitigation: realistic traffic evals + shadow testing + drift monitors; curriculum refresh.
Process collapse (e.g., verbose CoT, tool spam, premature stopping) Mitigation: efficiency penalties + stop-condition grading + tool budget constraints.
Safety regression from pushing task success too hard Mitigation: safety grader is non-negotiable gate; rollout requires passing safety suite.

Governance posture (how you keep control)

Permissions & approvals: restrict tool scopes; require HITL for high-impact actions.
Audit trails: log prompts/contexts/tool calls/outputs/grades/checkpoint IDs.
Rollout strategy: offline → shadow → canary with automatic rollback on KPI/safety thresholds.
Change management: every new grader/rubric is a “policy change” → version it, review it, and re-run regression.