Prolonged Reinforcement Learning

• Prolonged Reinforcement Learning (ProRL) is a framework that extends standard RL to long-duration, non-stationary environments by introducing mechanisms to sustain exploration and prevent catastrophic forgetting.
• It employs novel algorithmic approaches such as experience retention, temporal abstraction, and model-based planning to enhance sample efficiency and robust decision support.
• Empirical results in robotics, clinical decision support, language modeling, and resource allocation demonstrate ProRL's significant improvements in stability, efficiency, and generalization over extended periods.

Prolonged Reinforcement Learning (ProRL) refers to reinforcement learning regimes, architectures, and methodologies designed to maintain robust learning, adaptation, and generalization over extended timescales or in settings characterized by persistent, sequential, or evolving challenges. ProRL arises in diverse contexts ranging from continual adaptation to non-stationary environments, active learning in robotics and healthcare, decision support in high-dimensional systems, and the expansion of reasoning in LLMs. Core features of ProRL include mechanisms to avoid premature convergence, sustain exploration, mitigate catastrophic forgetting, leverage experience retention, and promote framework efficiency for prolonged, scalable deployment.

1. Problem Domains and Motivations

ProRL is motivated by settings in which standard RL algorithms encounter limitations due to protracted horizons, environmental non-stationarity, high-dimensional observation spaces, or delayed/prolonged action effects. Key problem domains include:

• Lifelong robotics, where tasks arrive sequentially and prior experience must be reused rather than forgotten (Xie et al., 2021).
• Non-stationary systems, where workload or process dynamics drift over time, demanding continual adaptation and knowledge retention (Hamadanian et al., 2022, Xie et al., 2020, Rank et al., 15 Feb 2024).
• Clinical decision support, where long durations and delayed effects complicate policy learning (e.g., mechanical ventilation weaning or drug dosing) (Prasad et al., 2017, Basu et al., 2023).
• Large-scale LLM reasoning, where RL must scale up across many tasks to expand a model’s capabilities while maintaining diversity, avoiding reward hacking, and supporting continual improvement (Liu et al., 30 May 2025, Liu et al., 16 Jul 2025, Fei et al., 2 Jul 2025).
• Resource allocation and control where persistent uncertainty and dynamic constraints preclude the use of fixed, predefined methods (Gandhi et al., 2021, Bakhshi et al., 2021).

The prolonged nature of these settings leads to requirements for sample efficiency, robust exploration, preservation of prior knowledge, and avoidance of mode collapse or overfitting.

2. Formulations and Frameworks

Multiple formalizations underpin ProRL. The Dynamic Parameter Markov Decision Process (DP-MDP) models a setting where task/environment parameters evolve according to latent stochastic processes, requiring RL agents to learn representations of these changes and condition their policies accordingly (Xie et al., 2020). In clinical control and drug dosing, the prolonged effects of actions violate the Markov assumption, necessitating augmented state representations or novel POMDP subclasses such as PAE-POMDP (Basu et al., 2023). Performative RL extends classical RL by capturing history-dependent environment changes in response to deployed policies, modeling the gradual drift of transition probabilities and reward functions (Rank et al., 15 Feb 2024).

A general ProRL agent may thus operate under:

$$R^\pi(s_t) = \lim_{T\to\infty} \mathbb{E} \left[ \sum_t \gamma^t r(s_t, a_t) \right]$$

Where the reward $r(s_t, a_t)$, state $s_t$, and transition kernel $P(s_{t+1}|s_t, a_t)$ may depend on latent environment variables $z$, histories of previous actions, or the agent’s policy deployment history.

3. Algorithmic Approaches and Techniques

Several RL algorithmic innovations support prolonged learning:

Experience Retention and Reuse

Lifelong RL employs experience retention where raw transitions from prior tasks are stored and selectively reused through importance weighting and filtering, enabling sample-efficient adaptation to new tasks and environments (Xie et al., 2021).

Off-Policy and Batch Learning

Off-policy batch RL—fitted Q-iteration (FQI) and variants—leverages historical data, allowing updates across all transitions in each iteration. This batch approach is robust for clinical RL under data scarcity and is coupled to function approximators such as Extra-Trees or neural networks (Prasad et al., 2017).

Latent Variable and Representation Learning

Structured latent variable models condition policies on inferred environment states, enabling agents to “track” environment shifts and anticipate changes. The Lifelong Latent Actor-Critic (LILAC) method integrates latent inference with off-policy RL (Xie et al., 2020).

Temporal Abstraction and Proactive Control

Methods such as TempoRL extend the agent’s action space to include a “temporal commitment” parameter, enabling the agent to decide when to act and skip over intermediate states, thereby reducing decision overhead and propagating rewards over n-step skips (Biedenkapp et al., 2021).

Model-Based Planning

Frameworks like RADAR (Resource Allocation via moDel leARning) combine real and synthetic samples, using background and decision-time planning with learned models to maximize sample efficiency in dynamic, high-complexity tasks (Bakhshi et al., 2021).

Policy Regularization and Reference Resets

In RL for LLMs, controlled KL regularization with periodic reference policy resets sustains exploration and entropy during prolonged training (Liu et al., 30 May 2025, Liu et al., 16 Jul 2025). Decoupled clipping bounds and dynamic sampling strategies further stabilize policy updates.

Modular Structures and Masking

Modulating masks allocate task-specific sub-networks within a fixed backbone, strongly reducing catastrophic forgetting and facilitating fast composition for new tasks (Ben-Iwhiwhu et al., 2022).

Diffusion and Kernel-Based Uncertainty Modeling

Gaussian Process Diffusion Policy combines generative diffusion models with kernel-based uncertainty estimation, guiding action selection toward high-reward behaviors while preserving exploration in distributionally shifted states (Horprasert et al., 16 Jun 2025).

4. Empirical Results and Practical Impact

ProRL methodologies demonstrate marked improvements in several domains:

Domain	Metric	ProRL Impact
ICU weaning (Prasad et al., 2017)	Reintubation/physiological stability	RL policy matched to clinical decisions → lower reintubation rates
Robotic lifelong learning (Xie et al., 2021)	Task success, sample efficiency	>2× sample efficiency, incremental success in real robots
LLMing (Liu et al., 30 May 2025, Liu et al., 16 Jul 2025)	pass@k, challenge-specific accuracy	Up to 54.8% improvement in logic puzzles, robust out-of-distribution performance
Resource allocation (Bakhshi et al., 2021, Gandhi et al., 2021)	Average profit, efficiency	Up to 44% gain, 97.7% efficiency, reduced computational cost
Model-based RL (Xie et al., 2020)	Cumulative reward under non-stationarity	LILAC outperforms SAC, PPO, SLAC in shifting domains

These results substantiate claims regarding efficiency, stability, reasoning boundary expansion, and robustness across non-stationary and prolonged horizons.

5. Technical Challenges and Limitations

Challenges intrinsic to ProRL include:

• Catastrophic forgetting in environments with changing or sequential tasks. Mitigated via modular architectures, experience retention, and expertise isolation (Ben-Iwhiwhu et al., 2022, Hamadanian et al., 2022).
• Sample inefficiency due to the curse of dimensionality, particularly relevant in large observation spaces. Addressed by hierarchical decomposition, reward or policy shaping from human/LLM feedback, and temporal abstraction (Laleh et al., 20 Nov 2024, Biedenkapp et al., 2021).
• Overfitting and mode collapse after extensive training; countered by regularization mechanisms and uncertainty-aware exploration (Horprasert et al., 16 Jun 2025, Liu et al., 30 May 2025).
• Computational and resource scaling in high-dimensional settings; SwiftRL demonstrates hardware-centric acceleration using Processing-In-Memory systems (Gogineni et al., 7 May 2024).

Some approaches (e.g., prioritized replay) may introduce inefficiency in highly uncertain environments due to amplification of erroneous predictions (Gandhi et al., 2021). GPR-based components pose scalability barriers due to cubic complexity; sparse approximations are suggested as future improvements (Horprasert et al., 16 Jun 2025).

6. Future Directions and Research Opportunities

Active areas of research in ProRL include:

• Automated task boundary detection and task-agnostic skill transfer for lifelong RL (Ben-Iwhiwhu et al., 2022).
• Integration of deeper function approximators into model-based RL frameworks for further sample efficiency gains (Bakhshi et al., 2021).
• Adaptive exploration strategies that independently govern skip, action, and observation decisions (Biedenkapp et al., 2021).
• Scaling kernel-based uncertainty models to large datasets (Horprasert et al., 16 Jun 2025).
• Hybrid approaches combining model-free and model-based RL in safety-critical domains via planning and cycle-consistency (Liu et al., 31 Jul 2024).
• Efficient RL reward signal generation (e.g., self-guided process rewards) and step-wise advantage estimation in process reinforcement learning (Fei et al., 2 Jul 2025).

Model weight and code releases (e.g., Nemotron-Research-Reasoning-Qwen-1.5B) are facilitating reproducibility and external validation of prolonged reinforcement learning approaches (Liu et al., 30 May 2025, Liu et al., 16 Jul 2025).

7. Integration of Human, LLM, and Feedback Signals

Recent survey work highlights the value of augmented feedback signals from humans and LLMs for optimizing decision-making and sample efficiency in ProRL (Laleh et al., 20 Nov 2024). Strategies span:

• Reward shaping: $$R(s,a) = R_{\text{env}}(s,a) + \lambda R_{\text{feedback}}(s,a)$$
• Hierarchical policies: decomposition of tasks via natural language subgoals.
• Dynamic policy blending: $$\pi_{\text{combined}}(a|s) \propto \pi_{\text{rl}}(a|s)^\alpha \cdot \pi_{\text{feedback}}(a|s)^{1-\alpha}$$
• Real-time corrective loops in autonomous domains.

These augmentations improve exploration efficiency, resilience to environmental shifts, and attention-focusing in high-dimensional spaces, mitigating prolonged learning periods and enhancing adaptability.

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Prolonged Reinforcement Learning encompasses a diverse set of architectures, algorithms, and theoretical advances that robustly generalize RL for long duration, shifting, or sequential challenges. Mechanisms for regularization, memory retention, modularity, and adaptive planning are central to current progress, with empirical results supporting tangible improvements in reasoning, efficiency, and robustness across domains. The ongoing fusion of domain knowledge, model structure, and feedback offers a fertile foundation for future developments in scalable, resilient RL.