Offline & Model-Based RL
Stabilizing Value Learning and Policy Extraction in Sparse-Reward Offline Reinforcement Learning
Generated automatically from the limitations stated in 5 papers (ICML, NeurIPS, ICLR), listed under Evidence. It is not a paper, and it does not come from papers submitted to CSPaper.
The problem
Current offline reinforcement learning algorithms suffer from catastrophic training instability, late-training policy collapse, and extreme variance on sparse-reward, long-horizon tasks such as AntMaze. To achieve reported benchmark numbers, practitioners routinely resort to unprincipled workarounds, including online pre-training interactions, domain-specific hyperparameter schedules, and checkpoint selection via test-environment rollouts. Without stable training dynamics under sparse feedback, offline RL cannot be deployed in high-stakes or real-world settings where offline checkpoint selection and online environment querying are strictly prohibited.
Why it matters
Reliable, hands-off offline reinforcement learning deployments on long-horizon sparse-reward environments without requiring oracle checkpoint selection or online tuning.
Ways to approach it
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- 1
Diagnostic ablation of value propagation dynamics: Systematically measure temporal-difference error propagation, critic divergence, and target value explosion across training iterations on D4RL AntMaze variants to isolate whether instability stems from out-of-distribution value overestimation, gradient norm explosion, or actor-critic policy extraction mismatch.
- 2
Value regularization and conservative target filtering: Implement and evaluate spectral norm bounds, value difference penalties, or distributional critic parameterizations on standard offline benchmarks, measuring training run variance, final success rate without oracle checkpoint selection, and late-training performance degradation.
- 3
Adaptive uncertainty-weighted temporal-difference updates: Design an intrinsic temporal-consistency or reward-propagation mechanism that handles long zero-reward trajectories without requiring task-specific critic pre-initialization, evaluated across sparse-reward navigation (AntMaze) and sparse manipulation (Adroit) benchmarks.
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Why it might fail
If the observed instability is fundamentally an insurmountable sample coverage deficit in existing static datasets rather than an algorithmic value-propagation defect, meaning no purely offline training objective can prevent value ambiguity on unvisited state transitions without active exploration.
Evidence
Each paper's own statement of the limitation, verbatim.
- Return-to-Go Is More Than a Number: Q-Guided Alignment for Return-Conditioned Supervised LearningICML 2026
Sparse-reward AntMaze performance relies on additional critic initialization/preprocessing (IQL-style updates for antmaze), and a robust sparse-reward variant is not developed
- Online Pre-Training for Offline-to-Online Reinforcement LearningICML 2025
Requires environment interaction for Online Pre-Training (25k steps) plus a kappa schedule and dataset-quality-dependent tuning; performance on Antmaze-large degrades substantially with fixed kappa (75.4 vs 90.6 on large-diverse), so per-domain scheduling is needed
- Behavior-Regularized Diffusion Policy Optimization for Offline Reinforcement LearningICML 2025
Training shows instability on some antmaze datasets due to sparse reward, with high variance on antmaze-umaze-diverse (83.0±12.4)
- Efficient Diffusion Policies For Offline Reinforcement LearningNeurIPS 2023
Training on AntMaze can succeed then crash, as shown by divergence between OMS and RAT metrics, indicating training instability
- Diffusion Policies as an Expressive Policy Class for Offline Reinforcement LearningICLR 2023
Sensitive to η: Adroit needs small η, AntMaze needs proper tuning; training on sparse-reward AntMaze shows large variance, requiring offline checkpoint selection
Nearest existing work
- A Closer Look at Offline RL AgentsNeurIPS 2022
- Instabilities of Offline RL with Pre-Trained Neural RepresentationICML 2021
- Offline Reinforcement Learning with Universal Horizon ModelsICML 2026
- Uncertainty Weighted Actor-Critic for Offline Reinforcement LearningICML 2021
- MOReL: Model-Based Offline Reinforcement LearningNeurIPS 2020
- RAMBO-RL: Robust Adversarial Model-Based Offline Reinforcement LearningNeurIPS 2022
- VIPO: Value Function Inconsistency Penalized Offline Reinforcement LearningICML 2026
- Regularizing a Model-based Policy Stationary Distribution to Stabilize Offline Reinforcement LearningICML 2022
- Adversarial Model for Offline Reinforcement LearningNeurIPS 2023
- Iteratively Refined Behavior Regularization for Offline Reinforcement LearningNeurIPS 2024
- Offline RL Without Off-Policy EvaluationNeurIPS 2021
- A Minimalist Approach to Offline Reinforcement LearningNeurIPS 2021
- Bayesian Design Principles for Offline-to-Online Reinforcement LearningICML 2024
- Offline Model-based Adaptable Policy LearningNeurIPS 2021
- Horizon Reduction Makes RL ScalableNeurIPS 2025
Related open problems
Offline & Model-Based RL
Cross-Modality and Sim-to-Real Robustness Evaluation of Offline and Model-Based Reinforcement Learning
Current offline and model-based reinforcement learning algorithms are almost exclusively benchmarked on state-observation continuous-control locomotion tasks in simulation (e.g., standard MuJoCo suites). Because papers routinely restrict evaluations to low-dimensional proprioceptive states, the performance, sample efficiency, and stability of these algorithms remains unknown when applied to visual observations, discrete action spaces, manipulation tasks, or physical platforms. Consequently, practitioners cannot determine whether published algorithmic gains reflect genuine advancements in decision-making or overfitting to low-dimensional simulation dynamics.
Offline & Model-Based RL
Cross-Domain Evaluation and Robustness of Offline Model-Based RL Across State-Action Representations
Offline and model-based reinforcement learning algorithms are almost universally developed, theoretically analyzed, and benchmarked exclusively on either discrete (tabular/gridworld) or continuous (continuous control) domains. Because individual methods are rarely evaluated across alternative state-action representations or stochasticity regimes, it is currently unknown whether standard conservatism mechanisms, planning rollouts, and uncertainty bounds remain robust outside their design scope. As a result, practitioners facing mixed, hybrid, or converted state-action spaces cannot anticipate failure modes or know whether existing offline RL algorithms transfer reliably.
Offline & Model-Based RL
Empirical Robustness of Model-Based RL Across Simulator Differentiability, Parallelism, and Task Horizons
Current model-based RL and planning algorithms are developed under disjoint simulation assumptions, variously requiring fully differentiable GPU physics engines, massive parallel batch environments, or short-horizon dense rewards. Because these methods are rarely tested outside their native simulator regimes, practitioners cannot determine whether algorithms relying on analytic dynamics or extreme parallel throughput transfer to standard non-differentiable environments or long-horizon agentic tasks. This leaves the empirical boundaries and failure modes of existing model-based approaches unknown across differing simulator capabilities.