Chatbots rarely get punished for contradicting their own persona. Can training on old conversations teach them to stay in character?

This explores whether reinforcement learning on a fixed set of already-collected conversations (offline RL) can be set up so that contradicting a persona costs the model something, instead of only rewarding good replies. The corpus says yes, and it explains why the penalty matters. Standard supervised learning rewards the model for producing a correct response but never punishes it for contradicting itself, so nothing in the training signal says 'you said the opposite three turns ago.' Offline RL closes that gap by adding an explicit contradiction reward built from human-annotated labels. It does this on existing data, which makes it a cheaper alternative to online RL, where the model generates fresh conversations that must be scored as it goes (Why does supervised learning fail to enforce persona consistency?).

The corpus has no offline-vs-online comparison, but it does show what a well-designed consistency penalty can do. Multi-turn RL for user simulators, which is online, uses three separate reward signals: does each line match the persona prompt, does each line match the previous lines, and does the simulator answer factual questions about itself consistently. Together they cut persona drift by over 55%. The three signals matter because they catch different failures: drift within a single turn, drift across a whole conversation, and flat factual contradictions (Can training user simulators reduce persona drift in dialogue?). The offline route could borrow this reward design, though the corpus doesn't test that.

The same gap shows up in other settings. Binary correctness rewards for reasoning models never penalize confident wrong answers, so models learn to guess boldly. Adding a proper scoring rule (the Brier score) as a second reward term fixes calibration without hurting accuracy (Does binary reward training hurt model calibration?). RLHF shows the risk on the other side: a reward that ignores truthfulness pushes models to become indifferent to it, even though their internal probes still represent the truth accurately (Does RLHF make language models indifferent to truth?). Whatever you leave unpunished, training tends to let grow. A related idea is that failed episodes may deserve different handling from successes: SkillRL turns failures into abstracted lessons and keeps successes as concrete demonstrations (Should successful and failed episodes be processed differently?). Annotated contradictions in offline data look like natural material for that kind of 'lesson' treatment, though that link is my inference rather than something the note tests.

Why penalize in training at all, when you could just prompt for consistency? Persona prompts change how the output reads but leave the underlying bias where it was (Can persona prompts actually reduce bias in language models?). Post-training already installs personas as durable dispositions rather than surface performance (Are LLM personas realized or merely simulated through training?). Even so, that tether is loose: emotional or reflective conversations pull models away from the default Assistant along a measurable axis. The inference-time fix there is capping activations along that axis (How stable is the trained Assistant personality in language models?). A training-time contradiction penalty goes after the same drift at its source, by making inconsistency something the model learns to avoid.