LLM-driven Modular Reward Generation

• LLM-driven Modular Reward Generation is a framework that modularizes reward functions to efficiently construct reward signals for reinforcement learning.
• It utilizes LLMs for trajectory sampling and code generation, creating scalable and data-efficient alternatives to manual reward engineering.
• Applications span agent planning, robotics, and dialogue alignment, enhancing interpretability, transferability, and automated self-alignment.

LLM-driven modular reward generation denotes a design paradigm in which reward models, functions, or decompositions are produced, refined, and orchestrated by LLMs—in an architecture where each reward-related functionality is encapsulated as a distinct, reusable module. The resulting pipelines enable efficient, flexible, and often fully autonomous construction, composition, and application of reward signals for reinforcement learning (RL), sequential decision-making, planning, and agent alignment scenarios. These systems, which include both learned parametric models and on-the-fly code generation or trajectory-level scoring, provide a scalable and data-efficient alternative to manual reward engineering and to hand-crafted preference modeling in RL from human feedback (RLHF) and broader agent systems.

1. Architectural Principles and Motivations

LLM-driven modular reward generation frameworks separate the process of reward formation into interacting modules—each targeted at a specific subproblem: trajectory sampling, intent synthesis, reward code generation, feedback distillation, reward decomposition, or preference aggregation. This modularization is motivated by:

• Data efficiency: Decoupling reward modeling from policy learning enables data reuse and reduces the requirement for online annotations or environment-specific data collection (Chen et al., 17 Feb 2025, Sun et al., 18 Oct 2024).
• Generalization and robustness: Modular reward models—trained on synthetic or replayed agent data—generalize better across held-out or out-of-distribution agent tasks compared to directly fine-tuned policies (Xia et al., 25 Feb 2025).
• Controllability and interpretability: Modular rewards allow for explicit adjustment (e.g., weighting for cost, efficiency, or safety objectives) and for transparent reward auditing (Chen et al., 17 Feb 2025, Mukherjee et al., 20 Nov 2025).
• Automation and scalability: By leveraging LLMs’ code-synthesis, natural language understanding, and reasoning capabilities, complex reward specification and iterative refinement processes are automated, reducing dependence on human-engineered reward signals (Sun et al., 18 Oct 2024, Lin et al., 6 Feb 2025, Cardenoso et al., 24 Nov 2025).

2. Canonical Modular Pipelines: Key Components and Workflow

Several instantiations of the modular LLM-driven reward generation paradigm have been advanced:

A. Multi-stage triplet-based reward model training (Chen et al., 17 Feb 2025):

• Trajectory Generator LLM: Autonomously samples reasoning traces and action-observation sequences in the environment given raw instructions.
• Intent Synthesizer LLM: Summarizes the task actually achieved, producing a refined, trajectory-specific intent.
• Negative Trajectory Synthesizer LLM: Given a positive trajectory and refined intent, generates "near-miss" negative trajectories by localized perturbation.
• Reward Model (e.g., VILA-based): Trained on (intent, positive, negative) triplets to score action trajectories, via a pairwise preference loss:

$$L(\theta) = - \mathbb{E}_{(x,h^+,h^-)} \left[ \log \sigma( \mathcal{R}_\theta(x,h^+) - \mathcal{R}_\theta(x,h^-) ) \right]$$

where $\sigma(z) = 1/(1+e^{-z})$.

• Test-time planning: Reward scores are used in best-of-$N$, Reflexion, or MCTS-based search, and reward heads can be replaced or modified for controllability of secondary objectives.

B. Reward Observation Space Evolution with cross-iteration memory (Heng et al., 10 Apr 2025):

• LLM-generated reward functions each induce a compact observation space (selected state variables + operations).
• State Execution Table: Tracks per-state usage and utility over all prior successful designs, breaking the Markovian constraint of standard LLM prompting.
• Sampling and refinement: State/operation refinements and candidate selection are adaptively prioritized using hybrid curiosity-success scores and sub-problem disentangling.
• Iterative feedback: Combined with adaptively thresholded heuristic feedback, this pipeline incrementally stabilizes and expands exploration.

C. Code-centric reward composition, feedback, and introspection (Sun et al., 18 Oct 2024, Baek et al., 15 Feb 2025):

• Coder module (LLM): Produces Python reward function code as modular sub-rewards, structurally labeled and composable.
• Evaluator module: Runs policy rollouts and generates feedback on reward code via process, trajectory, and preference-based signals.
• Preference Evaluation (e.g., TPE test): Provides order-preserving checks between successful and failed policies.
• Self-alignment: Modifies reward code directly in response to feedback summaries, targeting calibration of weights or decision thresholds without human intervention.

3. Mathematical Formalism and Training Objectives

Most frameworks formalize LLM-driven modular reward construction as the composition or optimization of parametric or non-parametric mappings from environments, observations, or policy trajectories to scalar or vector-valued reward signals:

• Triplet-based loss (Chen et al., 17 Feb 2025): See above.
• Preference logistic loss (Bradley–Terry) (Lin et al., 6 Feb 2025):

$$L(\theta) = - \mathbb{E}_{(s_a,s_b,y)}\Big[ y\log\sigma(\Phi(s_a) - \Phi(s_b)) + (1-y)\log\sigma(\Phi(s_b) - \Phi(s_a))\Big]$$

• MSE regression for explicit/implicit reward modeling (Xia et al., 25 Feb 2025):

$$\mathcal{L}_{\mathrm{MSE}}(\phi) = \frac{1}{|\mathcal{D}|} \sum_{(s,V)\in\mathcal{D}} (f_\phi(s)-V)^2$$

• Per-turn or per-module aggregation (Lee et al., 21 May 2025, Sun et al., 18 Oct 2024, Wang, 23 Jan 2024):

$R(s,a) = \sum_i r_i(s,a;\theta_i)$

• Heuristic bonus schemes: Intrinsic reward as novelty bonus from progress-state discretization (Sarukkai et al., 11 Oct 2024):

$$r_{\mathrm{count}}(s) = \frac{1}{\sqrt{N(\phi(s))}}$$

with total reward $$R_{\mathrm{total}}(s,a) = R_{\mathrm{extrinsic}}(s,a) + \lambda_c \cdot r_{\mathrm{count}}(s')$$.

• Planning integration: Reward modules enter exact (e.g., UCT for MCTS) or approximate (best-of-$N$, beam search) planning criteria, possibly under additional constraints such as trajectory length, cost, or compositionality (reward = objective - penalty × control metric).

4. Application Domains and Empirical Insights

LLM-driven modular reward pipelines are now used across diverse domains:

• Interactive agent planning and decision-making (WebShop, ScienceWorld, Game of 24, ALFWorld, AgentClinic): Demonstrated improvements over sampling/greedy policies on all core benchmarks; with Llama-70B, MCTS-augmented reward increased average success from 38.0% (sampling) to 49.3% (MCTS-guided) (Chen et al., 17 Feb 2025).
• Robotics and manipulation (Meta-World, ManiSkill2): Iterative reward refinement with dynamic feedback outperformed or matched expert-designed rewards on 10/12 tasks, achieving near-oracle performance with an order of magnitude fewer tokens than evolutionary search (Sun et al., 18 Oct 2024).
• Procedural content generation (PCG): Modular reward design with modular feedback, reasoning-based prompt engineering, and self-alignment raised RL-based PCG agents’ task accuracy by 415% vs. baseline on story-level content generation (Baek et al., 15 Feb 2025).
• Dialogue alignment and return decomposition: LLM-based reward decomposition (per-turn implicit reward from global returns) yielded substantial gains in human-evaluated conversation quality over classical reward decomposition and hand-crafted features (Lee et al., 21 May 2025).
• Cyber defense and MARL: Modular, persona-conditioned reward tables generated by LLM per agent class, easily recomposed for new threat-defender matchups, produced significant improvements in time-to-impact and defense rate against diverse adversaries (Mukherjee et al., 20 Nov 2025, Lin et al., 6 Feb 2025).
• Generalist and unsupervised RL reward design: LLM-only agent pipelines such as LEARN-Opt, which never access the environment source code or hand-crafted metrics, discover reward functions rivaling or exceeding those from EUREKA, including in high-variance, hard-to-specify control settings (Cardenoso et al., 24 Nov 2025).

5. Modularity Benefits, Limitations, and Prospective Extensions

Benefits:

• Abstraction and Composability: Reward modules (e.g., sub-rewards for efficiency, safety, goal completion) are individually editable, composable, and inspectable. Planning or control objectives can be traded off directly via scalarization or gating.
• Iterative refinement: Modular architectures facilitate successive LLM-guided editing—either over code, weights, or operational criteria—enabling data-driven self-alignment, robustness to edge-case failures, and adjustment as application requirements evolve.
• Cross-domain transferability: Reward head libraries, modular intent synthesizers, or preference modeling heads can be directly reused or lightly retrained for novel domains or agent models.
• Interpretability and controllability: Explicit decomposition and labeled code modules enable systematic reward auditing, correction of misalignment, and transparent enforcement of desiderata or hard constraints (Chen et al., 17 Feb 2025, Wang, 23 Jan 2024).

Limitations:

• Edge-case sensitivity: Quality and generalization of learned modular rewards depend on the diversity and representativeness of synthetic trajectory data; rare or adversarial edge cases may be missed.
• Incomplete constraint handling: Reward models may overlook secondary or implicit constraints present in raw instructions or task environments (e.g., hidden product features, latent world knowledge) (Chen et al., 17 Feb 2025).
• Scalability: Full self-play or feedback loops can be expensive in high-dimensional or continuous-control domains. LLM inference for triplet or preference labeling remains bottlenecked by token throughput in some settings.
• Prompt engineering overhead: While LLMs automate most reward specification, careful prompt structuring, code wrapping, and feedback chain design still require modeling expertise.

Extensions:

• Compositional reward heads: Multiple modular heads (r₁ for safety, r₂ for efficiency, r₃ for preference structure) blended for richer objectives.
• Knowledge integration and calibration: Reward modules can be augmented with knowledge retrievers or LLM calibration loops for improved constraint adherence or causal consistency.
• Hierarchical and subgoal reward modeling: Embedding explicit sub-reward or sub-goal learners within the modular framework enables scaling to complex, multi-level environments and agents.
• Self-training and self-filtering: Reward modules that filter and relabel synthetic trajectories in iterative loops, further closing the supervision gap (Chen et al., 17 Feb 2025).

6. Comparison with Alternative and Prior Paradigms

• Versus purely hand-crafted rewards: Modular LLM-based rewards consistently close or surpass the gap to expert heuristics, while requiring less domain engineering and providing clearer explicit module-level control (Sun et al., 18 Oct 2024, Chen et al., 17 Feb 2025).
• Versus monolithic scalar or pairwise rewards: Structured modular and hierarchical decompositions deliver stronger, more interpretable alignment, particularly for multi-faceted agent objectives (alignment, diversity, safety, fairness) (Chen et al., 17 Feb 2025, Heng et al., 10 Apr 2025).
• Versus end-to-end LLM policy fine-tuning: Reward-model fine-tuning, and test-time guidance (best-of-$N$, beam search) with frozen policies, yield greater generalization across held-out tasks and agents, and more robust transfer under policy or environment shift (Xia et al., 25 Feb 2025).

7. Concluding Observations

LLM-driven modular reward generation synthesizes the strengths of code-synthesis, natural language understanding, and algorithmic planning in unified agent reward design frameworks. This paradigm provides a rigorous, automated, and extensible pathway for aligning RL agents and decision models to broad, multi-objective, and data-efficient reward structures—unlocking significant improvements in agent performance, controllability, and alignment transparency across contemporary RL and agentic architectures (Chen et al., 17 Feb 2025, Xia et al., 25 Feb 2025, Sun et al., 18 Oct 2024, Heng et al., 10 Apr 2025).