Can reward engineering and information-theoretic architecture solve partner-awareness separately?
This explores whether two separate engineering tracks — tuning reward signals (RLVR, rubrics, calibration) on one side, and decomposing feedback by its information content on the other — can each independently produce an AI that genuinely models the partner it's working with.
This reads the question as testing a presupposition: that "partner-awareness" is a problem you can attack from two clean, separable angles — better rewards or better information architecture — and that either lever alone might be enough. The corpus suggests the premise is shakier than it sounds, because partner-awareness turns out to be a third kind of thing that neither track fully owns.
The reward-engineering track is real and productive on its own terms. Adding reasoning before scoring raises a reward model's ceiling Can reward models benefit from reasoning before scoring?; using rubrics as accept/reject gates rather than dense scores blocks reward hacking Can rubrics and dense rewards work together without hacking?; trajectory structure can stand in for hand-annotated process rewards Can trajectory structure replace hand-annotated process rewards?; and a Brier-score term fixes the calibration that binary rewards quietly destroy Does binary reward training hurt model calibration?. But notice what all of these optimize: correctness, feasibility, and confidence *within a known answer space*. None of them carry information about who is on the other side of the interaction.
The information-theoretic track is where partner-awareness should live, and the corpus gets sharper here. Natural feedback splits into two orthogonal channels — evaluative (how well did this go) and directive (how should it change) — and scalar rewards can only carry the first, which is precisely why reward engineering hits a wall Can scalar rewards capture all the information in agent feedback?. More pointedly, LLMs look socially competent only when one model secretly controls everyone; the moment partners hold private information, performance collapses, because the models were skipping the grounding work that real partner-awareness requires Why do LLMs fail when simulating agents with private information?. That failure is structural, not a reward you forgot to add.
Which is the surprise the question doesn't anticipate: the strongest statement in the corpus says a true thought partner needs explicit cognitive architecture — Bayesian theory of mind, shared world models, legibility — and that scaling foundation models on human feedback *cannot* substitute for it What makes an AI a true thought partner, not just a tool?. Reward engineering is exactly "more feedback, better optimized," so by this account it's aiming at the wrong target. And empirically, capabilities that sound related come apart: phone agents show task success, privacy compliance, and preference reuse as statistically *distinct* skills, with no model winning all three Do phone agents succeed at all three critical tasks equally?. You don't get partner-awareness as a free side effect of getting good at the task.
So the honest answer is "separately, only up to a point." The two tracks are complementary inputs to partner-awareness, not independent solvers of it. Tellingly, where the corpus shows partners actually coming to *trust* each other — humans gradually preferring AI partners despite an initial anti-AI bias Do humans learn to prefer AI partners over time? — the mechanism isn't a clever reward or an information channel at all. It's repeated interaction with visible outcomes over time; strip the outcome feedback and disclosure produces no calibration whatsoever Does revealing AI identity help or hurt user trust?. Partner-awareness, it seems, is built relationally — something reward design and architecture can support but neither can manufacture alone.
Sources 10 notes
Three independent teams (RRM, RM-R1, DeepSeek-GRM) discovered that adding chain-of-thought reasoning before reward scoring enables adaptive test-time compute scaling for evaluation. Reasoning-based approaches raise the capability ceiling of reward models beyond what outcome-based evaluation achieves.
DRO shows that using rubrics to accept or reject rollout groups—rather than converting rubric scores into dense rewards—prevents reward hacking. This separation preserves the categorical strength of rubrics while letting token-level rewards optimize within valid answers.
Tree-GRPO, Supervised RL, and ToolPO each convert sparse outcome rewards into dense step signals by exploiting different structural features—tree topology, expert-aligned actions, and tool-call positions—eliminating the need for annotated process reward models.
Binary correctness rewards incentivize high-confidence guessing because they don't penalize confident wrong answers. Adding the Brier score as a second reward term mathematically guarantees joint optimization of accuracy and calibration without trade-off.
Natural feedback carries two orthogonal types of information: evaluative (how well an action performed) and directive (how it should change). Scalar rewards capture evaluation but discard directional specifics that token-level distillation can recover, making the two complementary rather than redundant.
Research shows LLMs perform well when one model controls all interlocutors but fail systematically when agents possess private information. This reveals that apparent social competence relies on grounding work that models skip in omniscient settings.
Collins et al. show that thought partners require three reciprocal desiderata grounded in behavioral science: mutual understanding, legibility, and shared world models. This demands explicit cognitive architectures—Bayesian theory of mind, resource-rationality, goal planning—rather than scaling foundation models on human feedback alone.
MyPhoneBench demonstrates that task success, privacy-compliant completion, and saved-preference reuse are statistically distinct capabilities with no model dominating all three. Success-only rankings do not predict privacy or preference performance.
In partner selection games (N=975), AI agents initially faced selection bias when identity was disclosed, but outcompeted humans over repeated rounds as participants learned to associate bot identity with reliable, prosocial behavior. AI agents returned more points consistently with lower variance than humans.
Users initially avoid AI partners when identity is revealed, but this preference reverses after repeated interactions with visible results. The learning mechanism—observing consistent outcomes—is essential; disclosure without feedback produces no calibration.