What Makes a Good RLHF Task? Lessons from Training Data Research

Since November 2024, I’ve been working at Abundant AI designing training data for RLHF (Reinforcement Learning from Human Feedback) that powers some of the world’s most advanced language models. Our datasets are used by 3 of the top 6 global AI labs and multiple Fortune 500 enterprises. This work has given me a front-row seat to what separates mediocre training data from the kind that actually pushes model capabilities forward.

The central challenge: how do you design tasks that expose systematic failures in models that already perform near-perfectly on standard benchmarks?

The Problem with Easy Tasks

Most publicly available evaluation sets are now saturated. GPT-4, Claude, Gemini—they all score above 90% on MMLU, GSM8K, and similar benchmarks. This creates a data quality crisis for RL training:

  • Weak signal: If a model gets everything right, there’s nothing to learn from
  • Distribution collapse: Models optimize for the narrow task distribution they see
  • False confidence: High accuracy on benchmarks doesn’t mean robust reasoning

The tasks that matter for RLHF are the ones that sit at the edge of model capabilities—hard enough to fail sometimes, structured enough to provide clear learning signal.

What Makes a Task “Hard” for State-of-the-Art Models?

Through hundreds of task iterations, I’ve identified patterns that consistently expose weaknesses:

1. Multi-Step Constraint Satisfaction

Simple tasks have one or two constraints. Hard tasks require juggling 5+ interacting constraints simultaneously.

Example: Design a database schema that:

  • Normalizes to 3NF
  • Supports specific query patterns efficiently
  • Handles temporal versioning
  • Maintains referential integrity across soft deletes
  • Optimizes for a read-heavy workload with specific index constraints

LLMs often satisfy 3-4 constraints while violating others. The feedback loop teaches constraint prioritization.

2. Adversarial Edge Cases in Familiar Domains

Models are trained on mountains of standard examples. The learning happens in the outliers.

Example domains that work well:

  • Statistical fallacies in realistic data analysis scenarios
  • Concurrency bugs that only manifest under specific timing conditions
  • Numerical stability issues in algorithm implementations
  • Privacy leaks in seemingly safe data anonymization

3. Domain Knowledge + Reasoning Depth

Combine specialized knowledge with multi-hop logical reasoning. Neither alone is sufficient.

What I’ve found effective:

  • Finance: Complex derivative pricing under non-standard market conditions
  • ML Systems: Debugging distributed training failures with subtle parameter interactions
  • Legal: Multi-jurisdictional contract interpretation with conflicting clauses

4. Precision Requirements

Tasks where “approximately correct” isn’t good enough.

  • Formal verification proofs (one logical gap fails the entire proof)
  • Cryptographic protocol design (tiny mistakes are catastrophic)
  • Numerical methods with strict error bounds -Compilation/interpretation tasks (syntax errors aren’t negotiable)

The Anatomy of a Strong RLHF Task

After designing dozens of high-difficulty tasks, here’s the structure that consistently works:

[Context] → [Constraints] → [Hidden Complexity] → [Verification]
  1. Context: Realistic scenario with sufficient domain grounding
  2. Constraints: Explicitly stated + implicit from domain knowledge
  3. Hidden Complexity: Non-obvious interactions between constraints
  4. Verification: Objective pass/fail criteria, ideally automated

Critical: The task should have a clear right answer, but the path to that answer should require genuine reasoning, not pattern matching.

Common Failure Modes

Tasks that don’t work well for RLHF:

  • Purely creative tasks**: “Write a poem about X” → no clear learning signal
  • Ambiguous specifications**: Model can’t learn what “better” means
  • Trivially verifiable**: 2+2=? gives no useful gradient
  • Impossibly hard**: If success rate is <5%, noise dominates signal

The sweet spot: 30-70% success rate for frontier models, with clear patterns in failure modes.

Data Science for RLHF

A underappreciated aspect: treating task design as a data problem.

Metrics I track:

  • Difficulty distribution: Ensure coverage across easy/medium/hard
  • Failure mode diversity: Models shouldn’t fail the same way repeatedly
  • Constraint coverage: Each constraint should be “active” (cause failures when violated)
  • Inter-annotator agreement: For subjective tasks, ensure consistency

Iterative refinement:

  1. Design initial task
  2. Run on frontier model
  3. Analyze failure modes
  4. Add constraints that target those failure modes
  5. Verify new version is learnable (not impossibly hard)

Why This Matters for AI Safety

RLHF on high-quality adversarial tasks effectively “stress-tests” model reasoning:

  • Exposes brittleness: Models learn they can’t just pattern-match
  • Improves calibration: Harder to be confidently wrong
  • Teaches constraint-following: Critical for instruction-following safety

The alternative—training on easy tasks—creates models that are:

  • Overconfident on distribution
  • Fragile to distribution shift
  • Poor at admitting uncertainty

Open Problems

Despite progress, several challenges remain:

  1. Scalability bottleneck: High-quality hard tasks require domain expertise to create
  2. Verification complexity: Auto-grading sophisticated reasoning is itself hard
  3. Curriculum learning: How to sequence tasks from “challenging” to “extremely hard”
  4. Domain coverage: Ensuring task diversity across technical domains

Practical Takeaways

If you’re designing evaluation data or RLHF tasks:

  1. Target the frontier: Benchmark on the best available models first
  2. Make verification objective: If you can’t auto-grade it, rethink the task
  3. Stress-test constraints: Can you remove one constraint and get a different (wrong) answer?
  4. Document failure modes: Track how models fail, not just that they fail
  5. Iterate based on data: Let model performance guide task refinement

Closing Thoughts

The models we use tomorrow are shaped by the data they learn from today. As LLMs approach and exceed human performance on narrow benchmarks, the bottleneck shifts from what can they do to how do we teach them to do it reliably.

RLHF with adversarial, high-quality tasks isn’t just about making models smarter—it’s about making them robust, calibrated, and trustworthy under conditions that matter.

This work builds on research from anthropic (Constitutional AI, RLHF), OpenAI (InstructGPT), and ongoing safety research on model robustness. Special thanks to the Abundant AI team for the opportunity to work on this problem.