Reinforcement Learning Fine-Tuning

• Reinforcement Learning Fine-Tuning (RL-FT) is a post-training paradigm that adapts large pre-trained models using reinforcement signals to improve task performance and mitigate catastrophic forgetting.
• It integrates knowledge retention techniques such as Behavioral Cloning, Kickstarting, and Elastic Weight Consolidation to preserve rarely visited skills across varied state distributions.
• Empirical studies in domains like NetHack and robotics demonstrate that applying retention losses during fine-tuning yields significant performance gains and enhanced stability.

Reinforcement Learning Fine-Tuning (RL-FT) is a post-training paradigm in which policies, often represented as large neural models, are adapted through reinforcement signals to improve generalization, task performance, or alignment beyond what is achievable by supervised fine-tuning alone. RL-FT has emerged as a dominant approach for transferring pre-trained model capabilities across tasks in domains including language, vision, robotics, and scientific discovery. The methodology, mechanisms of knowledge retention, and implications for representation and sample efficiency have been studied extensively across a variety of challenging environments and models.

1. Catastrophic Forgetting and the RL-FT Paradigm

Fine-tuning in reinforcement learning differs fundamentally from supervised learning due to the key role of data distribution shifts induced by agent actions. In RL-FT, catastrophic forgetting of pre-trained capabilities is a primary impediment to successful transfer: as the agent’s policy adapts to the limited state space visited early in fine-tuning, parameters “forget” previously learned behaviors in rarely visited states, eroding the benefits of pre-training (Wołczyk et al., 5 Feb 2024). Two gaps are central:

• State Coverage Gap: New data distributions emphasize “close” states while “far” states (mastered during pre-training) are rarely revisited. Policy updates then drift away from behaviors optimal in the far region.
• Imperfect Cloning Gap: If the pre-trained policy is an imperfect copy of an expert (e.g., suboptimal behavioral cloning), fine-tuning exacerbates biases by focusing on newly encountered states, accelerating deterioration in the less explored state subspaces.

Empirical analysis in NetHack and Montezuma’s Revenge environments confirms that naive RL-FT erases skills in rarely visited regions, while knowledge retention techniques can preserve or even enhance these abilities, yielding new state-of-the-art results (e.g., NetHack performance exceeding 10K points from a 5K baseline).

2. Knowledge Retention Techniques in RL-FT

Mitigating forgetting requires augmenting standard RL fine-tuning with explicit knowledge retention losses. The principal approaches are:

• Behavioral Cloning (BC) Loss: Maintains a KL-divergence between the fine-tuned policy and the pre-trained (expert) policy evaluated on states from the pre-training distribution:

$$L_{\text{BC}}(\theta) = \mathbb{E}_{s \sim B} \left[ D_{\mathrm{KL}} \left( \pi_*(s) \| \pi_\theta(s) \right) \right]$$

where $B$ is a buffer of expert states.

• Kickstarting (KS): Applies a similar KL loss but with data sampled online from the current policy, thereby aligning the fine-tuned policy with itself while retaining expert-likeness.
• Elastic Weight Consolidation (EWC): Penalizes deviation of important parameters from pre-training via Fisher matrix-based weighting:

$$L_{\text{EWC}}(\theta) = \sum_i F_i (\theta_i^* - \theta_i)^2$$

with $F_i$ the diagonal of the Fisher Information Matrix.

Choice of technique and tuning of buffer, replay, or weighting hyperparameters are task-dependent. In domains exhibiting an “imperfect cloning gap” (e.g., NetHack), kickstarting outperforms BC; with state coverage gap predominant (e.g., Montezuma’s Revenge), BC is preferred. These strategies are readily integrated into modern on-policy RL algorithms (APPO, PPO), and are necessary for robust transfer across evolving state distributions (Wołczyk et al., 5 Feb 2024).

3. Empirical Characterization and Performance Benchmarks

Empirical studies validate the theoretical insights:

Environment	Forgetting Observed	Effective Retention Method	SOTA Gain
NetHack (Human Monk)	Loss of skills in deep dungeon levels	Kickstarting (KS)	+100% (5K→10K pts)
Montezuma’s Revenge	Sudden drop in far-room success rate	Behavioral Cloning (BC)	Maintained pretrain
RoboticSequence	Coverage-driven forgetting	BC	Higher stability

Density and trajectory plots indicate that naive RL-FT collapses visitation to initial or shallow states; inclusion of BC/KS preserves far-state mastery. Auxiliary losses safeguard skills even when the fine-tuning task presents divergent state visitation frequencies relative to pre-training.

Performance improvements are not limited to a single domain. Techniques validated in NetHack and Montezuma’s Revenge generalize to robotics and complex sequential tasks, with empirical best practices specifying which retention method to deploy in various transfer regimes.

4. Theoretical Underpinnings and Mathematical Formalism

The catastrophic forgetting phenomenon in RL-FT emerges as a function of the non-i.i.d. sampling in RL: actions taken by the adapted policy constrain the state distribution, leading to “local” adaptation and interference. The mathematical apparatus formalizes auxiliary loss integration, e.g., adding $L_{\text{EWC}}$ or $L_{\text{BC}}$ to the RL objective as a regularizer, or as combined multi-objective optimization. The effectiveness of regularization terms is contingent on the proper estimation of the Fisher matrix or the quality and coverage of the replay buffer for expert data.

This forgetting mitigation framing extends continual learning theory into the RL fine-tuning context, drawing an explicit connection between RL-FT, catastrophic interference, and the need for regularized adaptation strategies traditionally employed in task-sequential learning.

5. Design Recommendations and Broader Implications

Standard RL-FT pipelines must be redesigned to systematically address forgetting. Key recommendations include:

• Routine Application of Retention Losses: Embedding BC, KS, or EWC components in all RL fine-tuning runs, especially where downstream task state-distributions differ from pre-training.
• Buffer Management: Maintaining an expert or pre-training data buffer representative of high-performing subspaces.
• Monitoring Visitation Frequency: Analyzing empirical state coverage during fine-tuning for early detection of catastrophic drift.

Applicability and impact extend to any RL setting where state visitation or reward structure induces distributional changes not covered in pre-training, including sparse-reward exploration, complex decision-making, and robotic manipulation.

Furthermore, findings inspire continued investigation of hybrid retention strategies and continual-learning-inspired RL formulations, seeking to address open challenges in sample efficiency, stability under heavy distribution shift, and unlocking the full potential of pre-trained policy models across diverse downstream tasks.

6. Conclusion and Outlook

RL-FT is fundamentally an exercise in the preservation and controlled adaptation of previously acquired capabilities. Forgetting is not merely a secondary concern—it is the central bottleneck impeding effective transfer. Empirical and theoretical studies demonstrate that standard knowledge retention techniques are essential, double state-of-the-art benchmark scores, and should be considered integral to the RL fine-tuning toolkit (Wołczyk et al., 5 Feb 2024). As RL-FT becomes ubiquitous across domains, rigorous mitigation of catastrophic forgetting will be required for robust, sample-efficient, and generalizable transfer learning in large-scale reinforcement learning systems.