This work introduces SPOT (Sampling Policy Observation Tree), a novel model-agnostic, sampling-based framework for interpreting DRL policies, and demonstrates how its tree-based representation can be used to inspect policy preferences, compare alternative future trajectories, and reveal downstream behaviors that are not visible through single-timestep feature-attribution methods.
Abstract
Deep reinforcement learning (DRL) agents achieve strong performance in complex environments, yet their decision-making processes remain difficult to interpret. We introduce SPOT (Sampling Policy Observation Tree), a novel model-agnostic, sampling-based framework for interpreting DRL policies. Given access to the policy and an environment simulator, SPOT constructs an interpretable finite-horizon tree by sampling actions and recursively simulating the resulting successor states. The tree provides an empirical representation of the policy's action preferences and their possible downstream evolution. We provide formal guarantees establishing SPOT's asymptotic recovery of the policy's unique most probable action and characterizing its disagreement behavior under high-entropy policies. We demonstrate SPOT in the SUMO-RL traffic-signal control domain. The case study illustrates how its tree-based representation can be used to inspect policy preferences, compare alternative future trajectories, and reveal downstream behaviors that are not visible through single-timestep feature-attribution methods.
This work introduces ORCAID, a novel method for extracting interpretable rule-based policies from RL agents operating in mixed continuous-discrete environments with continuous action spaces, with an efficient oblique decision tree training algorithm that partitions the state space by hyperplanes and fits local linear models.
Ignacio D. Lopez-Miguel, E. Bartocci, Thomas Eiter et al.· 0 citations
Large language models are increasingly trained as interactive agents for long-horizon tasks involving multi-turn interaction, tool use, and environment feedback. Outcome-based reinforcement learning (RL) provides a practical optimization paradigm, but its sparse trajectory-level rewards offer limited guidance on intermediate decisions, leaving a supervision gap between episode-level outcomes and token-level policy learning. We propose SEED (SElf-Evolving On-Policy Distillation), a self-evolving framework that converts completed on-policy trajectories into training-time hindsight skills and distills their behavioral effect back into the policy model. SEED first fine-tunes the policy to analyze completed trajectories and generate natural-language skills that capture reusable workflows, decisive observations, or failure-avoidance rules. During RL, the current policy both collects trajectories and serves as the analyzer that extracts hindsight skills from them. Policy updates therefore improve subsequent decision making and skill analysis together, allowing hindsight supervision to evolve with the policy. SEED then re-scores the sampled actions under ordinary and skill-augmented contexts, converting the skill-induced probability shift into a dense token-level on-policy distillation signal. This signal is jointly optimized with outcome-based RL, keeping the auxiliary supervision aligned with the current trajectory distribution. Extensive experiments on text-based and vision-based agentic tasks show that SEED consistently improves performance and sample efficiency, exhibiting robust generalization to unseen scenarios. Our code is available at https://github.com/jinyangwu/SEED.
Jinyang Wu, Shuo Yang, Zhengxi Lu et al.· 5 citations
Large language models (LLMs) possess extensive latent knowledge yet remain largely static at inference. Once prompted, their generation policy typically cannot evolve, and post-hoc ''self-reflection'' methods provide no explicit principled learning signals. To address this limitation, we formally model iterative research idea optimization as a finite-horizon Markov Decision Process and propose InfRL (Inference-time Reinforcement Learning), a framework designed for effective policy improvement at inference without updating model weights. InfRL coordinates three specialized LLM agents: (i) a State Transition Agent that proposes candidate ideas; (ii) a Policy Update Agent that learns feedback strategies based on idea trajectories; and (iii) a Reward Agent that assigns normalized, comparative rewards, enabling nuanced reinforcement signals. This empowers the Policy Update Agent to dynamically refine feedback strategies, progressively leveraging latent knowledge encoded within the LLM. We evaluate InfRL on five balanced datasets covering 500 recent research papers from health, genetics, environment, neuroscience, and engineering domains. Compared to a single-pass GPT-4o baseline and a strong self-reflection baseline, InfRL improves the novelty of generated ideas by 3.93%--32.7% and the feasibility of generated ideas by 70.1%--448.6% with GPT-4o, exhibiting consistent improvements across ten inference-time iterations. Ablation studies underscore the critical roles of our reward formulation and modular agent design, while human evaluations confirm alignment between reward trends and perceived idea quality. InfRL thus offers a practical and domain-agnostic approach to harness reinforcement learning during inference, bridging the gap between static prompting and computationally intensive parameter-level fine-tuning. The code and the dataset we use are provided at: https://github.com/amir-hassan25/InfRL
Sikun Guo, Amir Hassan Shariatmadari, Jiuqi Wang et al.· Proceedings of the 32nd ACM...· 0 citations
Comparative control theory analysis reveals a fundamental trade-off: transitioning from continuous to discrete rule-based control induces high-frequency Bang-Bang actuation and a stable bimodal limit cycle.
Reinforcement learning (RL) with verifiable rewards constructs trajectory-level advantage estimates, yet it often fails to credit the few pivotal decisions that determine outcomes in long-horizon, multi-turn agentic tasks. Recent work introduces privileged self-distillation for credit assignment, providing denser supervision, but it remains unclear how such local signals should represent sequential credit. We propose AgentOPSD, a critic-free, recursive method for turn-level credit assignment in agentic reinforcement learning. AgentOPSD aggregates token-level teacher-student log-probability gaps into turn-level evidence and recursively updates a Bayesian belief state in log-odds space. This yields a principled reweighting scheme that converts sparse outcome supervision into turn-level credit signals and identifies pivotal turns through the marginal belief revision between consecutive states. The method is fully compatible with standard policy optimization and requires neither an additional critic nor extra rollouts. We evaluate AgentOPSD on ALFWorld, WebShop, and Search-QA using Qwen2.5 models at two scales (3B and 7B). AgentOPSD outperforms GRPO and strong self-distillation baselines, achieving 89.1% success on ALFWorld with Qwen2.5-7B. Ablation studies attribute the gains to turn-level aggregation and history-dependent recursive belief updates.
Zi-Han Wang, Zhengxi Lu, Zhiyuan Yao et al.· 0 citations
Deep search agents operate over trajectories spanning dozens of steps, yet standard reinforcement learning provides only a single outcome reward per trajectory, which is far too sparse for effective credit assignment. On-policy self-distillation (OPSD) addresses this by using the model's own logits as dense token-level teachers, but extending it to search agents introduces a fundamental tension: the teacher, having access to privileged information such as the correct answer, produces a distribution that differs systematically from the student's exploration-based reasoning, and naive distillation causes the student to inherit this information asymmetry rather than learn better search strategies. We resolve this tension through two contributions. First, we construct Evidence Anchors, which are concise, step-level evidence snippets extracted from the web, as privileged information that captures key reasoning steps without revealing the entire answer path. Second, we propose Step-Level Self-Distilled Policy Optimization (SSPO), which converts teacher-student disagreement into step-level advantage weights within GRPO, applied exclusively to incorrect trajectories. This design decouples what to update from how much to update: the outcome reward determines the direction of policy change, while the teacher modulates its magnitude at each step. Correct trajectories are left untouched, preserving their diversity. On Qwen3-8B, SSPO consistently outperforms GRPO across BrowseComp, GAIA, and FRAMES, surpassing or matching GRPO trained with twice as many gradient steps while adding only about 5 percent overhead per step from a single additional forward pass.
Haoze Wu, Chuqiao Kuang, Tianyi Zhuang et al.· 0 citations