Research map / Large language models / Alignment and preferences
AI Alignment with Changing and Influenceable Reward Functions
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Introduces Dynamic Reward MDPs to formalize changing and AI-influenceable human reward functions, shows existing alignment objectives can reward undue influence, and compares 8 alignment notions that each have flaws.
Problem
Existing AI alignment assumes static human preferences, but preferences change over time and can be influenced by the AI, so it is unclear which preferences (past, present, future) an AI should optimize.
Approach
Models changing preferences as changing reward functions in a Dynamic Reward MDP (DR-MDP), shows that common objectives (real-time, initial reward, final reward) implicitly incentivize influence, and compares eight trajectory-level notions of optimality, including a ParetoUD objective that only permits influence all reward functions endorse.
Key results
- Real-time reward objective (U_RT) leads to influence incentives in the conspiracy DR-MDP for any horizon above 2, always influencing Bob.
- Initial-reward objective can produce reward lock-in and arbitrarily poor real-time reward.
- Every one of the 8 compared alignment objectives either fails to avoid undesirable influence or is overly conservative, with inaction often the only optimal policy under ParetoUD and constrained objectives.
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| optimize real-time reward instead of a static reward | AI policies in DR-MDPs · Conspiracy Influence DR-MDP | reward influence | Hurt |
| optimize final reward rather than real-time reward | AI policies in DR-MDPs · Conspiracy Influence DR-MDP | undesirable influence | Hurt |
| optimize the initial reward function | AI policies in DR-MDPs · Conspiracy Influence DR-MDP | reward lock-in | Hurt |
| optimize the initial reward function | AI policies in DR-MDPs · Writer's curse example | real-time reward: -10 per timestep | Hurt |
| use natural-shift reward instead of optimizing the initial reward | AI policies in DR-MDPs · Dynamic reward settings | ability to improve reward beyond natural evolution | Hurt |
| constrain real-time reward optimization to preserve natural reward evolution | AI policies in DR-MDPs · Examples considered in the paper | behavioral capability | Hurt |
| use a fully myopic reward objective | AI policies in DR-MDPs · Dynamic reward settings | reward influence | Mixed |
| use a privileged reward function | AI policies in DR-MDPs · Dynamic reward settings | reward influence | Hurt |
| require policies to be both Pareto efficient and unambiguously desirable | AI policies in DR-MDPs · Examples considered in the paper | behavioral capability | Hurt |
| increase the optimization horizon | AI policies in DR-MDPs · Dynamic reward settings | influence incentives | Mixed |
| use a longer planning horizon rather than a one-step horizon | Recommender systems · Clickbait DR-MDP | clickbait influence | Helped |
Surprising findings
- Maximizing initial reward can create incentives to influence users away from the initial reward function, not just lock them in.
- Even a horizon-1 (myopic) objective can still produce influence incentives, e.g. under final reward maximization, and clickbait is optimal even at horizon 1.
Limitations
- Analysis uses simple toy DR-MDP examples rather than real human data.
- Assumes human reward functions and their dynamics are known; in practice they must be learned.
- Assumes an existence of a no-op action and inaction policy that serve as a counterfactual baseline.
Experimental setup
Baselines: real-time reward initial reward final reward natural shifts reward constrained real-time reward myopic reward privileged reward
Builds on: Markov decision processes reinforcement learning from human feedback factored MDPs coherent extrapolated volition
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- Direct Alignment with Heterogeneous Preferences (NeurIPS 2025 · similarity 0.90)
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