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PARL: A Unified Framework for Policy Alignment in Reinforcement Learning from Human Feedback
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Proposes PARL, a bilevel optimization framework for RLHF-style policy alignment where the upper-level alignment objective depends on trajectories sampled from the lower-level optimal policy, with the A-PARL algorithm achieving O(1/T) convergence and reporting up to 63% sample savings on DMControl and MetaWorld.
Problem
Policy alignment in RL from utility or human preference feedback: existing RLHF pipelines ignore that the data used to evaluate the reward/alignment objective depends on the current policy, causing distribution shift and misaligned rewards.
Approach
Formulates policy alignment as a bilevel problem in which the upper-level alignment objective's expectation is over trajectories generated by the lower-level optimal policy, derives hypergradients through the policy (via implicit function theorem), and solves it with a basic bilevel gradient algorithm (A-PARL).
Key results
- A-PARL reaches near-oracle episodic return faster than PEBBLE and PEBBLE+SURF on Walker, DoorOpen and ButtonPress, averaged over 5 seeds.
- Reports up to 63% reduction in samples required for policy alignment.
- Theory: O(1/T) rate for average squared upper-level gradient norm under non-convex upper objective without lower-level strong convexity.
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use A-PARL instead of PEBBLE and PEBBLE+SURF | Policy alignment · DeepMind Control Suite and MetaWorld tasks | required samples: up to 63% | Helped |
| Use A-PARL instead of PEBBLE and PEBBLE+SURF | Policy alignment · Walker, DoorOpen, and ButtonPress | episodic return | Helped |
Surprising findings
- PEBBLE+SURF's gains depend heavily on unsupervised data and augmentations, so comparison with it is not fully fair, as the authors note.
Limitations
- Analysis assumes oracle access to gradients, Hessians and trajectory distributions; stochastic setting left as remark.
- Requires unique lower-level optimizer (tabular or softmax parameterization) and PL condition on the value function.
- Experiments use simulated human teachers rather than real human preferences.
Experimental setup
Datasets: DeepMind Control Suite (Walker) MetaWorld (DoorOpen, ButtonPress)
Benchmarks: DMControl Walker MetaWorld DoorOpen MetaWorld ButtonPress
Metrics: episodic ground-truth return sample efficiency
Baselines: PEBBLE PEBBLE+SURF Oracle (true reward)
Builds on: PEBBLE SURF RLHF Bradley-Terry model
Closest papers by meaning
- Query-Policy Misalignment in Preference-Based Reinforcement Learning (ICLR 2024 · similarity 0.90)
- Contextual Bilevel Reinforcement Learning for Incentive Alignment (NeurIPS 2024 · similarity 0.90)
- Meta-Reinforcement Learning with Universal Policy Adaptation: Provable Near-Optimality under All-task Optimum Comparator (NeurIPS 2024 · similarity 0.89)
- Iterative Preference Learning from Human Feedback: Bridging Theory and Practice for RLHF under KL-constraint (ICML 2024 · similarity 0.89)
- Projection Optimization: A General Framework for Multi-Objective and Multi-Group RLHF (ICML 2025 · similarity 0.88)
- Exploration-Driven Policy Optimization in RLHF: Theoretical Insights on Efficient Data Utilization (ICML 2024 · similarity 0.88)
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