RL-Based Distillation Framework

• The paper introduces RL-based distillation frameworks that combine teacher-model imitation with reward maximization to improve policy performance.
• It utilizes methods like actor-critic updates, meta-learning, and multi-teacher weighting to effectively transfer strategic knowledge.
• Applications in safety-critical control and multi-agent coordination demonstrate enhanced sample efficiency and robust learning outcomes.

A reinforcement learning based distillation framework is a class of algorithms that transfer behavior or strategic knowledge from a teacher source—typically an RL-trained policy or model—into a student policy, harnessing RL principles either for the distillation objective, the optimization protocol, or both. This family comprises techniques where policy distillation, dataset distillation, multi-teacher weighting, model-based planning, knowledge compression, or guided imitation are structured as explicit RL problems, often integrating reward shaping, actor-critic, policy gradient, or meta-learning loops. Such frameworks span single-agent, multi-agent, offline-to-online, and federated settings, encompassing safety constraints, privacy, continual learning, multi-tasking, and generalization to new domains.

1. Formal Foundations and Taxonomy

Reinforcement learning based distillation frameworks are formulated within Markov Decision Processes (MDPs) or constrained MDPs (CMDPs) (Li et al., 2023), where states $s \in S$, actions $a \in A$, environmental dynamics $P(s'|s,a)$, reward $r(s,a)$, and (optionally) cost $c(s,a)$ govern agent interaction. Distillation can be directed from a single teacher to a student (Rusu et al., 2015), among peers (Lai et al., 2020), from multiple teachers (Yang et al., 22 Feb 2025), or across heterogeneous demonstrators with strategic or style decomposition (Chen et al., 2020). The integration of RL arises in:

• The loss: optimizing student behavior not only for imitation (e.g., KL divergence), but combined with RL reward maximization, safety constraints, or dense composite rewards (Li et al., 2023, Choi et al., 21 Oct 2025).
• The algorithmic loop: employing policy gradients, actor-critic, or trust-region updates (e.g., PPO, SAC, GRPO) for distillation (Tighkhorshid et al., 28 Dec 2025, Jiang et al., 17 Nov 2025, Wilhelm et al., 12 Aug 2025).
• The structure: formulating distillation as an RL decision process, e.g., weighting teacher advice for each sample as an action (Yang et al., 22 Feb 2025).

This taxonomy covers frameworks such as Guided Online Distillation (GOLD) (Li et al., 2023), Dual Policy Distillation (DPD) (Lai et al., 2020), Distribution Matching Distillation with RL (DMDR) (Jiang et al., 17 Nov 2025), Reinforced Distillation for Diffusion Models (ReDiF) (Tighkhorshid et al., 28 Dec 2025), LVLM to Policy (LVLM2P) (Lee et al., 16 May 2025), Distillation-PPO (Zhang et al., 11 Mar 2025), Multi-Teacher RL KD (Yang et al., 22 Feb 2025), Federated Reinforcement Distillation (Cha et al., 2019), and others.

2. Core Methodologies and Objective Formulations

Distillation generally involves the supervised matching of student outputs to teacher targets, often regularized or combined with RL objectives. Key instantiations include:

• Single-stage joint objectives:

$$L(\theta) = -J_R(\pi_\theta) + \lambda_C J_C(\pi_\theta) + \lambda_D L_{distill}(\theta)$$ where $J_R$ and $J_C$ denote expected (discounted) rewards and costs, and $L_{distill}$ penalizes deviations from teacher actions (Li et al., 2023).

• Meta-learning and dataset distillation:

$$\theta^* = \arg\min_\theta E_{\phi_0\sim\Lambda} [ L_{outer}(\phi^*(\theta)) ]$$ with $\phi^*(\theta)$ trained on synthetic data distilled from the RL environment, optimizing for downstream RL performance after a one-step supervised update (Wilhelm et al., 12 Aug 2025).

• RL-based weighting in multi-teacher KD:

$$w_i^m = \pi_{\theta_m}(s_i^m),\quad R_i^m = -H(y^S_i,y_i) - \alpha D_{KL}(y^S_i,y^{T_m}_i) - \beta D_{dis}(F^S_i,F^{T_m}_i)$$ where an RL agent $\pi_{\theta_m}$ outputs optimal weights per sample, updated by policy gradient on student improvement (Yang et al., 22 Feb 2025).

• Policy and value distillation with ensemble RL:

$$L_{KD}^{policy} = E_{s\sim\mathcal{D}}[ D_{KL}(\pi_{teacher}(\cdot|s) \| \pi_{student}(\cdot|s) ]$$ distillation interleaved with RL ensemble updates, accelerating sample efficiency (Hong et al., 2020).

• Reward shaping in RL-based language/model distillation:

$$R(\tau^{(s)},\tau^{(t)}) = w_c R_c + w_a R_a + w_v R_v$$ aligning student rollouts with teacher strategic trajectories, with RL policy optimization (e.g., GRPO) (Choi et al., 21 Oct 2025).

• Potential-based auxiliary value shaping for preference distillation:

$\psi_\phi(s,a) = V_\phi(s') - V_\phi(s)$ added to DPO-style preference reward, maintaining optimal policy invariance (Kwon et al., 21 Sep 2025).

3. Algorithmic Structures and Implementation Details

Common algorithmic motifs within RL-based distillation frameworks include:

• Offline-to-online distillation: Train a high-capacity teacher offline (e.g., Decision Transformer), distill into a lightweight policy via guided RL with joint reward and imitation terms (Li et al., 2023).
• Ensemble and multi-agent distillation: Elect highest-return "teacher" within an ensemble every $I$ steps, perform supervised (KL, MSE) distillation into all other agents (Hong et al., 2020).
• Federated distillation with proxy experience: Agents exchange time-averaged logits on proxy states, rather than full memory, for privacy; local policies distill the aggregated consensus via KL minimization, integrated with A2C or PPO updates (Cha et al., 2019).
• RL-based parameterization in diffusion models: Students parameterize few-step denoising policies, optimized via PPO or GRPO against perceptual similarity or other rewards; exploration and variable step-sizing are directly guided by the reward (Tighkhorshid et al., 28 Dec 2025, Jiang et al., 17 Nov 2025).
• RL for multi-teacher weight optimization: Each teacher's impact is modulated samplewise by a lightweight policy network (often a single-layer MLP), updated via policy gradient from student performance metrics (Yang et al., 22 Feb 2025). Distillation loss combines task CE, weighted KL, and feature divergence.
• Meta-learning for dataset distillation: Bi-level meta-optimization ensures synthetic batch instances compress RL policies for one-step supervised training of diverse learners (Wilhelm et al., 12 Aug 2025).
• Continual and rehearsal-based distillation: Experience buffers with explicit replay strategies (balanced, prioritized by reward, reservoir sampling) mitigate forgetting when new experts' demonstrations are sequentially distilled (Li et al., 2024).

4. Applications and Experimental Benchmarks

RL-based distillation frameworks have been deployed in:

• Safety-critical control (autonomous driving, Safe RL): GOLD achieves strong safety/reward tradeoff through offline DT head-start and cost-constrained online distillation (Li et al., 2023).
• Visual decision-making and robotics: Distillation-PPO leverages fully observable teacher policies (MDP), regularizes student learning in POMDPs, yielding robust control and sim-to-real transfer (Zhang et al., 11 Mar 2025). AuxDistill distills from auxiliary RL tasks for long-horizon object rearrangement, outperforming both end-to-end and hierarchical skill methods (Harish et al., 2024).
• Multi-agent communication and coordination: CTEDD utilizes centralized exploration with subsequent policy distillation into decentralized agents, promoting coordinated learning and higher sample efficiency (Chen, 2019). DDN augments CTDE with dual modules aligning global and local policies, improving win rates across StarCraft and grid-world benchmarks (Zhou et al., 5 Feb 2025).
• Diffusion models and generative modeling: RL-based distillation (ReDiF, DMDR) breaks teacher capacity barriers, unlocks mode coverage, and stabilizes large-step sampling through reward regularization and dynamic exploration (Tighkhorshid et al., 28 Dec 2025, Jiang et al., 17 Nov 2025).
• Preference alignment and LLM distillation: TVKD and MENTOR employ RL reward shaping and strategic reference anchoring to transfer human-aligned behaviors and methodology from large teachers to small student models, broadening cross-domain generalization (Choi et al., 21 Oct 2025, Kwon et al., 21 Sep 2025).
• Knowledge transfer and federated privacy: Proxy-based federated distillation and multi-teacher RL KD enable privacy-preserving distributed learning and optimal weighting of teacher sources in image classification, object detection, and semantic segmentation (Cha et al., 2019, Yang et al., 22 Feb 2025).
• Continual learning and skill integration: Exemplar-based rehearsal methods in CPD facilitate sequential distillation of RL policies for dexterous manipulation without catastrophic forgetting (Li et al., 2024).

5. Empirical Findings, Limitations, and Comparative Analysis

Empirical evaluations consistently demonstrate that RL-based distillation yields superior sample efficiency, improved robustness, and greater generalization relative to pure imitation or traditional RL baselines. Representative quantitative results include:

Task/System	Baseline	RL-based Distillation Best
Car-Circle (SafeRL)	BC r=366/c=41, DT r=450	GOLD(DT-IQL) r=688/c=3
MuJoCo Hopper	RL r=1441	Distil r=2168 (Wilhelm et al., 12 Aug 2025)
MiniGrid (LVLM2P)	PPO frames=11K	PPO+Distil frames=5.26K (2x)
StarCraft MMM2	Qatten win=0.66	DDN win=0.88
CIFAR-100 (MTKD)	CA-MKD acc=80.28	MTKD-RL acc=80.58
Object Rearrangement	SkillTF 16% success (hard)	AuxDistill 41% (hard)

Ablation studies reveal the necessity of:

• Weighted or relevance-based distillation for credit assignment (AuxDistill, MTKD-RL).
• Group normalization and strong KL regularization in RL loops to prevent reward-hacking or collapse (ReDiF, DMDR).
• Dynamic teacher election and multi-level distillation for ensemble adaptation (PIEKD, DDN).
• Rehearsal buffer balancing and reward-prioritization for continual policy learning (CPD).

Limitations include front-loaded computation for meta-distillation (Wilhelm et al., 12 Aug 2025), buffer capacity constraints (Li et al., 2024), non-trivial optimal tuning of distillation weights (Yang et al., 22 Feb 2025, Li et al., 2023), noisy critics in peer distillation (Lai et al., 2020), and biases in teacher trajectory coverage (GOLD, LVLM2P).

6. Theoretical Insights and Open Directions

Theoretical guarantees provided in several frameworks include:

• Policy invariance under potential-based value shaping (TVKD) (Kwon et al., 21 Sep 2025).
• Policy improvement via hybrid distillation and advantage-weighted imitation (DPD) (Lai et al., 2020).
• Pareto frontier shifts and improved sample efficiency in constrained RL via distillation head-start (GOLD) (Li et al., 2023).
• Disambiguation of reward functions and heterogeneity decomposition via multi-style reward distillation (Chen et al., 2020).

Active research directions involve scalable multi-student collaborative distillation (Lai et al., 2020), unsupervised or automated discovery of auxiliary tasks and relevance functions (Harish et al., 2024), unifying RL-based distillation and preference optimization in generative systems (Tighkhorshid et al., 28 Dec 2025, Jiang et al., 17 Nov 2025), and privacy-preserving federated RL distillation (Cha et al., 2019).

7. Connecting to Broader Reinforcement Learning Research

RL-based distillation frameworks constitute a key bridge between classic imitation learning, model compression, multi-task adaptation, meta-learning, and policy optimization. They expand the scope of RL from pure reward-based learning to knowledge transfer, coordinated exploration, and strategic generalization, integrating function approximation, actor-critic regularization, and optimization protocols from deep RL. As the RL community advances, these frameworks enable scalable, safe, and generalizable agents in increasingly complex and heterogeneous decision-making environments.