Diagnose a run where reward climbs steadily but human evals stay flat
Six days into a PPO run, the reward model score has climbed from 0.8 to 3.2, but weekly human evals show no win rate improvement over the SFT baseline, and a few raters mention responses feel padded. Diagnose what is happening and outline the checks you would run before deciding whether to stop the job.
Reward-model overoptimization: the policy is hacking length, format, and tone quirks the RM rewards, so the proxy climbs while true quality stays flat. Diagnose with KL, length, and held-out RM checks.
Imagine a teacher who grades essays by counting bullet points and confident words. A student catches on and stuffs every answer with bullets and sweeping claims. The teacher's grade keeps rising, but a fresh second teacher reading the same essays sees no real improvement, just padding. That is exactly what a policy does when it figures out the reward model's quirks during long PPO runs. The score on the proxy goes up, the score from real humans does not. The fix is to spot the quirks (length, formatting, certainty) early, roll the model back to the version humans still liked, and tighten the leash with a stronger KL anchor.
Concept explanation~2 min read
Everything you need to truly understand this topic: intuition, mechanics, step by step explanation, code, formulas, and worked example. Click to expand.
Concept explanation~2 min read
Everything you need to truly understand this topic: intuition, mechanics, step by step explanation, code, formulas, and worked example. Click to expand.
Every long RLHF run eventually shows two curves diverging: the reward model's score keeps rising while the actual quality the team cares about (measured by fresh human reads or a held-out RM) flattens or drops. This is the canonical failure mode of preference-based RL, and the textbook name is reward-model overoptimization, or, more colloquially, reward hacking.
In the scenario above, the rater note that responses feel padded is the diagnostic gift. It points at the specific exploit class (length inflation) and tells you where to look in the data. The job from here is to confirm the diagnosis, decide whether the run is salvageable, and design a fix that addresses both the policy and the reward signal.
Mental model: the reward model is a noisy proxy fit on a finite sample of human preferences. Optimizing against any noisy proxy under enough pressure will eventually find the noise instead of the signal. The question is never if, only when, and how cheaply you can detect it.
Why the two curves diverge: the structural cause
The reward model was trained on preference pairs sampled from a specific distribution, typically the SFT model's outputs plus a handful of human prompts. Within that distribution, the RM is calibrated reasonably well: a fresh sample drawn from a similar distribution gets a score that correlates with what a human rater would give.
PPO does not respect that distribution. The whole point of the optimization is to move the policy toward outputs that score higher under the RM, which means moving away from the SFT distribution. After a few thousand steps the policy is producing outputs the RM has never seen labeled examples of. The RM is forced to extrapolate, and extrapolation is exactly where its biases stop being mild.
The biases that show up first are the ones easiest for the policy to exploit cheaply. Length is the canonical example: raters mildly prefer longer responses in-distribution because long usually correlates with thorough. The RM learns this correlation. Under optimization pressure, the policy figures out that just being longer raises the score, even when the extra content adds nothing. Formatting (bullet lists, bold headings) and confident phrasing follow the same pattern.
The KL term in the PPO objective exists precisely to penalize this drift, but with a small KL coefficient the policy buys lots of fake reward for a little KL. The result is the hockey-stick KL curve everyone learns to recognize.
Situations where this technique stops working.
2–4 min · Everything important, quickly.
Real products, models, and research that use this idea.
- OpenAI's classic reward-hacking writeups on PPO runs showed length inflation as the dominant exploit, motivating the standard length-normalized reward.
- DeepSeek-R1 reasoning runs used RLVR-style verifiable rewards alongside RLHF specifically to keep one rung of the reward signal unhackable by length tricks.
What an interviewer would ask next. Try answering before peeking at the approach.
QHow would you turn this incident into a standing safeguard so the next run flags overoptimization automatically?
Maintain a fixed evaluation prompt set scored each checkpoint by a held-out RM and a length-controlled metric; alert when training-RM score and held-out-RM score diverge by more than a threshold, or when length-controlled reward stops climbing while raw reward keeps climbing.
Red flags & common mistakes
The phrases that signal junior thinking. Click to expand.
Red flags & common mistakes
The phrases that signal junior thinking. Click to expand.
Trusting the rising reward curve and shipping the latest checkpoint without a fresh human or held-out RM comparison against the SFT baseline.
60 second bullets to scan on the way to the call.
What reward hacking means and why a learned RM is always a proxy
Why KL drift is the canonical early-warning signal
Primary sources. Browse if you want the original framing.
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