RL for Prompt Tuning in Language Models

• Reinforcement learning for prompt tuning is defined as formulating prompt generation as an MDP where discrete prompt tokens are sequential actions optimized through reward functions.
• Lightweight policy networks, typically small MLPs on frozen language models, generate tokens using algorithms like PPO and bandit methods to improve performance and interpretability.
• Empirical results show that RL-tuned prompts can enhance accuracy and compression, while transferring effectively across tasks and diverse architectures.

Reinforcement learning for prompt tuning refers to the systematic optimization of discrete or structured prompts for large-scale machine learning models—most often LLMs—using reinforcement learning (RL) algorithms. In this paradigm, the prompt construction process is formalized as a (potentially sequential) decision-making problem, where the objective is to discover prompts that elicit desirable model behaviors as quantified by external reward functions. This contrasts with conventional prompt engineering techniques, which depend on manual design or gradient-based continuous prompt embedding optimization.

1. Formulation of Prompt Tuning as a Reinforcement Learning Problem

RL-based prompt tuning formulates prompt selection or generation as an MDP (or contextual bandit, in one-step settings), with prompt tokens or structures as discrete actions. The agent interacts with a frozen backbone model (e.g., a LLM or graph neural network) by producing prompts, which, when fed into the model, yield outputs evaluated by a reward function reflecting downstream task quality or desired behaviors.

In RLPrompt (Deng et al., 2022), the agent uses a parameter-efficient policy network π₍θ₎ to generate prompt tokens zₜ conditioned sequentially on prior token choices. The RL objective is:

$$\max_\theta \mathbb{E}_{\tilde{z} \sim \prod_{t=1}^T \pi_\theta(z_t|z_{<t})} \left[ R(y_{LM}(\tilde{z}, x)) \right]$$

where $R(\cdot)$ is a task-specific reward calculated after running the LM with the complete prompt.

This general approach extends to settings including (but not limited to) few-shot classification, style transfer, dialogue generation, graph representations, and recommendation systems, with discrete prompt selection, compression, or construction treated as the agent’s action space (Su et al., 2022, Jung et al., 2023, Zhu et al., 6 Aug 2024, Xin et al., 2022).

2. Architectures and Policy Designs

RLPrompt (Deng et al., 2022) and its successors employ small policy networks that operate atop frozen LMs, typically as lightweight multi-layer perceptrons (MLPs) interfacing with the LM embedding spaces. For each token position, the policy produces a probability distribution over the vocabulary, often by projecting contextual LM embeddings through a trainable MLP and a frozen output head (e.g., LM’s token classifier matrix). Only the policy network’s parameters, typically a few percent of the LM parameters, are updated.

Extensions deploy more elaborate architectures: cooperative multi-agent frameworks decompose prompt construction into agent sub-sequences (Kim et al., 2023), while bandit-based schemes optimize prompt segments via contextual multi-armed bandits, learning reward models for individual prompt slots (Rietz et al., 7 Feb 2025, Rietz et al., 10 Feb 2025). For graph neural networks, RELIEF (Zhu et al., 6 Aug 2024) frames prompt selection as a hybrid discrete–continuous RL problem, using H-PPO to select nodes and corresponding additive feature prompts.

When prompt rewriting is necessary, RL is also used to train a rewriter model (often a separate LLM) that generates revised, higher-reward prompts by modifying or extending the original instruction (Kong et al., 16 Jan 2024, Li et al., 2023).

The policy’s action space can be:

• Sequences of tokens (ordered selections from the vocabulary)
• Edit actions on initial prompts (token inclusion/exclusion, deletions, insertions)
• Structured subprompt choices (subsection, node, in-context example)
• Generation of in-context examples or demonstrations (PRL (Batorski et al., 20 May 2025))

State representations vary accordingly, encoding the prompt-so-far, the query/task context, and in some cases the model’s internal representations or knowledge graphs (GRL-Prompt (Liu et al., 19 Nov 2024)).

3. Reward Engineering and Optimization Strategies

A fundamental challenge arises from the discrete, black-box nature of prompt-induced reward signals. Since the backbone model does not provide gradients with respect to the prompt (LLM weights being frozen), policy updates rely purely on RL objectives.

RLPrompt (Deng et al., 2022) employs input-specific z-score normalization to address reward scale variance:

$$z\text{-}score(z, x) = \frac{R_x(z) - \mathrm{mean}_{z' \in \mathcal{Z}(x)} R_x(z')}{\mathrm{std}_{z' \in \mathcal{Z}(x)} R_x(z')}$$

and designs piecewise rewards (e.g., hinge loss-like constructs) to prevent degenerate or adversarial prompt exploits.

Compression methods (PCRL (Jung et al., 2023)) design mixed rewards combining faithfulness (e.g., ROUGE-L between outputs under original and compressed prompts) and token count reduction:

$$R(p, a) = \begin{cases} |p^π|/|p| &\text{ if ROUGE-L} \geq \tau \ -\lambda &\text{ otherwise } \end{cases}$$

Reward engineering may include:

• Perplexity/fluency (dialogue generation (Su et al., 2022))
• Task accuracy or F1 (classification tasks (Kong et al., 16 Jan 2024, Batorski et al., 20 May 2025))
• Style/content similarity metrics (e.g., BERTScore, style classifier outputs (Jafari et al., 18 Feb 2024))
• Coverage and order-sensitive selection in in-context learning (GRL-Prompt (Liu et al., 19 Nov 2024))

For stability and exploration, entropy-based regularizations are deployed. Hard prompt interpretability is improved via sparse Tsallis entropy regularization (Choi et al., 20 Jul 2024):

$$S_2(\pi) = \mathbb{E}_\pi \left[ \frac{1 - \pi(z|z_{<t})}{2} \right]$$

yielding sparsemax-style policies that zero-out low-probability tokens.

4. Optimization Algorithms and Stability Enhancements

Policy optimization in RL-based prompt tuning predominantly leverages on-policy algorithms, especially variants of Proximal Policy Optimization (PPO (Su et al., 2022, Kong et al., 16 Jan 2024, Jie et al., 2023, Zhu et al., 6 Aug 2024)), sometimes with significant adaptations:

• Reward normalization and input-specific scaling mitigate reward stochasticity (Deng et al., 2022).
• KL-penalty with anchor models stabilizes policy updates, as in Adaptive PPO (APPO; StablePrompt (Kwon et al., 10 Oct 2024))—where a stable anchor policy is maintained to prevent harmful drift from spurious reward signals.
• Hybrid discrete–continuous PPO for mixed action spaces (as in graph node/location and prompt content in RELIEF (Zhu et al., 6 Aug 2024)).
• Bandit-based optimization (contextual or Thompson sampling) reduces sample complexity for prompt segment selection (Rietz et al., 7 Feb 2025, Rietz et al., 10 Feb 2025, Qu et al., 7 Jul 2025).
• Black-box gradient-free optimization via ranking/rank-based estimators (e.g. ZO-RankSGD (Hu et al., 2023)), amenable to settings where numerical gradients are unavailable.

Multi-objective RL methods systematically optimize over conflicting reward axes (e.g., content, style, sentiment). In MORL-Prompt (Jafari et al., 18 Feb 2024), Pareto volume maximization and multi-gradient descent algorithms ensure balanced trade-offs rather than optimization collapse to a dominant metric.

Cooperative multi-agent RL, as in MultiPrompter (Kim et al., 2023), decomposes the large prompt space into agent subspaces, with a centralized critic for credit assignment.

5. Empirical Outcomes and Emerging Properties

Across diverse tasks, RL-based prompt tuning yields numerically strong results with several unexpected properties:

• In few-shot text classification and style transfer, RLPrompt outperforms both hand-crafted and gradient-based prompt optimization, achieving higher accuracy and stability with fewer parameters updated (Deng et al., 2022).
• RL-learned discrete prompts frequently appear as ungrammatical “gibberish,” but demonstrate high transferability across different LM architectures—suggesting models internally parse prompts in non-human, semantically non-interpretable ways (Deng et al., 2022, Choi et al., 20 Jul 2024).
• Prompt compression via PCRL achieves an average 24.6% token reduction without degrading generation quality, with compressed prompts transferable across LMs (Jung et al., 2023).
• Dialogue prompt tuning via PPO can effectively steer black-box chatbots towards target emotions or topics, with multi-task learning boosting adaptability to new dialogue attributes (Su et al., 2022).
• In graph networks, selective and minimal prompt feature tuning via RL increases downstream performance—especially in few-shot regimes (RELIEF (Zhu et al., 6 Aug 2024)).
• Multi-objective prompt tuning overcomes single-metric collapse, with Pareto volume maximization yielding balanced style/content/sentiment outputs (Jafari et al., 18 Feb 2024).
• RL-based prompt rewriters can automatically generate human-readable, high-performance prompts for both personalization and general downstream tasks, often outperforming prompts from supervised or RL-only tuning (Li et al., 2023, Kong et al., 16 Jan 2024).

The table below contrasts selected methods by action space and key results:

Method	Action space	Key outcomes
RLPrompt	Token sequence	Outperforms manual/soft prompts; transfer
PCRL	Token-level selection	~25% shorter prompts, preserved quality
MultiPrompter	Subprompt (multi-agent)	Longer, more effective, interpretable
StablePrompt	Token sequence	State-of-the-art, robust via APPO/anchors
RELIEF	Node+feature (graph)	High data efficiency in few-shot

6. Transferability, Interpretability, and Limitations

A salient empirical finding is the transferability of RL-discovered prompts across LMs and architectures (Deng et al., 2022, Jung et al., 2023). Discrete prompts tuned on one model, or in one domain, can induce strong performance when ported to other settings, indicating a shared latent prompting “language.”

Despite strong task metrics, RL-discovered prompts are often non-interpretable by humans and may exhibit brittleness: prompt overfitting, where performance drops if the prompt form differs from RL training, is a vulnerability (Aissi et al., 25 Oct 2024). Contrastive regularization can improve robustness by aligning model representations across prompt variants.

Recent advances address interpretability (e.g., sparsemax regularization, explicit prompt rewriting (Choi et al., 20 Jul 2024, Kong et al., 16 Jan 2024)) and optimize not only for accuracy but for human-guided control, content, and interpretability.

Immediate limitations include:

• Instability and sensitivity in RL updates (ameliorated by anchor policies, reward normalization, and careful regularization (Kwon et al., 10 Oct 2024))
• High evaluation/inference costs in prompt search for large LMs (addressed by model-predictive, bandit-based, and surrogate evaluation frameworks (Qu et al., 7 Jul 2025))
• Nontrivial hyperparameter tuning for RL convergence and reward balancing
• Trade-off between interpretability and reward maximization in unconstrained prompt spaces

7. Outlook and Future Directions

Reinforcement learning for prompt tuning is now a central component in model alignment, efficient adaptation, and controllable generation. Ongoing and future research directions include:

• Scaling RL-based prompt tuning to even larger LMs and multi-modal settings (Choi et al., 20 Jul 2024, Aissi et al., 25 Oct 2024)
• Improved interpretability via sparsity, filtering, and contrastive representation learning
• Automated prior prompt engineering for reinforcement fine-tuning, systematically controlling style, reasoning, or task behavior during training (Taveekitworachai et al., 20 May 2025)
• Efficient online prompt selection by integrating bandit and Bayesian models with RL, reducing sample complexity in practical deployment (Rietz et al., 7 Feb 2025, Rietz et al., 10 Feb 2025, Qu et al., 7 Jul 2025)
• Multi-objective or human-in-the-loop prompt tuning for robust performance across tasks and fairness axes (Jafari et al., 18 Feb 2024)
• Graph-based and knowledge-aware prompt construction using RL for structured data and in-context learning (Liu et al., 19 Nov 2024)

This synthesis illustrates that RL-based prompt tuning—across discrete prompt optimization, prompt compression, rewriting, and few-shot demonstration selection—now underpins much of the emerging methodology for extracting and aligning the capabilities of large pre-trained models to downstream application requirements. The landscape is rapidly evolving, with RL frameworks providing the principled scaffolding needed for scalable, transferable, and increasingly interpretable prompt optimization.