Latent Action Reparameterization for Efficient Agent Inference
Wenhao Huang, Qingwen Zeng, Qiyue Chen, Zijie Guo, Yu Sun, Cheng Yang, Siru Ouyang, Jiri Gesi
Abstract
Large language model (LLM) agents often rely on long sequences of low-level textual actions, resulting in large effective decision horizons and high inference cost. While prior work has focused on improving inference efficiency through system-level optimizations or prompt engineering, we argue that a key bottleneck lies in the representation of the action space itself. We propose Latent Action Reparameterization (LAR), a framework that learns a compact latent action space in which each latent action corresponds to a multi-step semantic behavior. By reparameterizing agent actions into latent units, LAR enables decision making over a shorter effective horizon while preserving the expressiveness of the original action space. Unlike hand-crafted macros or hierarchical controllers, latent actions are learned from agent trajectories and integrated directly into the model, allowing both planning and execution to operate over abstract action representations. Across a range of LLM-based agent benchmarks, LAR significantly reduces the effective action horizon and improves inference efficiency under fixed compute budgets. As a consequence, our approach achieves substantial reductions in action tokens and corresponding wall-clock inference time, while maintaining or improving task success rates. These results suggest that action representation learning is a critical and underexplored factor in scaling efficient LLM agent inference, complementary to advances in model architecture and hardware.
AI Impact Assessments
(1 model)Scientific Impact Assessment: Latent Action Reparameterization for Efficient Agent Inference
1. Core Contribution
LAR proposes learning a compact latent action space where each latent action corresponds to a multi-step semantic behavior, thereby reducing the effective decision horizon of LLM agents. The key insight is that many agent actions contain structurally redundant, low-entropy components (system prompts, tool invocation syntax, recurring scaffolding) that can be collapsed into single vocabulary tokens without altering task-relevant behavior. The framework uses an entropy-based filter to identify "transition-equivalent" action segments from trajectories, assigns them dedicated vocabulary symbols, and trains the model via trajectory-level KL distillation with a LoRA adapter (0.1% of parameters). This reframes efficiency not as faster token generation but as operating over a more appropriate decision granularity.
2. Methodological Rigor
Strengths in methodology:
Weaknesses:
3. Potential Impact
Near-term applications:
Broader influence:
Limitations on impact:
4. Timeliness & Relevance
This paper addresses a genuine and growing bottleneck. As LLM agents are deployed in increasingly complex multi-step tasks (coding assistants, web automation, scientific workflows), inference cost becomes a primary scaling constraint. The observation that per-step optimizations don't address the fundamental issue of decision granularity is timely. The paper also aligns with growing interest in inference-time compute allocation and efficiency.
However, concurrent work on reasoning distillation, thinking token compression, and latent reasoning (e.g., Coconut, CALM) addresses adjacent problems. The paper could better position itself relative to these emerging approaches.
5. Strengths & Limitations
Key strengths:
1. Novel perspective: Reframing efficiency as an action representation problem rather than a generation speed problem is genuinely insightful.
2. Principled design: The entropy-based identification with executability constraints, dual-trajectory distillation, and progressive ablation form a coherent framework.
3. Generalization evidence: Held-out benchmark transfer (Table 2) and cross-domain unified training (Table 7) demonstrate that latent actions capture reusable structure.
4. Practical deployability: Zero overhead at inference (latent tokens processed identically to regular tokens), parameter-efficient training (0.1% parameters), and demonstrated industrial applicability.
Notable weaknesses:
1. Modest quantitative gains in some settings: Token reductions of 2.9-9.2% on certain benchmarks are relatively small; wall-clock improvements (Table 8) show marginal throughput gains.
2. Limited baselines for action abstraction: No comparison against macro-action learning methods or hierarchical planning approaches that also address decision granularity.
3. Scalability of the pipeline: The per-model, per-domain trajectory collection and identification process adds engineering overhead not fully characterized.
4. Missing statistical rigor: No error bars, confidence intervals, or significance tests on the main results.
5. The "latent" framing is somewhat misleading: The actions are more accurately described as "macro tokens" or "compressed action templates"—they are not truly latent in the representation learning sense (no continuous latent space, no variational inference).
Summary
LAR presents a compelling conceptual contribution—that action granularity is a bottleneck for LLM agent efficiency—backed by a practical framework and reasonable empirical validation. The approach is well-engineered and demonstrates generalization. However, the quantitative improvements are sometimes modest, statistical rigor is lacking, and the theoretical framework (transition equivalence) is only loosely connected to the implementation. The paper opens an interesting research direction but the current instantiation may have limited practical impact given the engineering overhead relative to gains.
Generated May 19, 2026
Comparison History (21)
Paper 1 provides a massive, high-quality, domain-specific dataset (152B tokens) addressing the critical scarcity of clean pretraining data for LLMs. Given the high value of the financial domain and the exhaustion of public web corpora, this dataset will likely be widely adopted for both foundational model training and specialized downstream applications, leading to broader real-world impact and higher citation potential than the methodological agent optimization proposed in Paper 2.
Paper 2 introduces a broadly applicable algorithmic framework (latent action reparameterization) that can improve efficiency and performance of LLM agents, with clear real-world deployment benefits (lower latency/cost) and potential impact across RL, planning, systems, and agentic AI. Its core idea is general and likely to be reusable and extensible. Paper 1 is timely and valuable for governance and auditing, but is more evaluation/pipeline-specific, depends on particular model specs, and may have narrower methodological generalizability and longer-term uptake than a new action-representation technique.
Paper 1 addresses a highly critical and timely bottleneck in the latest wave of LLM alignment (RLVR and GRPO) by introducing a conceptually novel distinction between human-assigned importance and optimization usefulness in rubric rewards. Improving the efficiency and effectiveness of RL optimization for complex model behaviors currently has massive implications for advancing reasoning models. While Paper 2 offers valuable efficiency gains for agents, Paper 1's foundational insights into reward modeling and dynamic signal adaptation are likely to have a broader and more immediate impact on state-of-the-art model training paradigms.
Paper 1 introduces a novel framework (LAR) addressing a fundamental bottleneck in LLM agent efficiency—action space representation—with broad applicability across agent benchmarks. It offers a concrete, generalizable method with demonstrated improvements in inference efficiency and task success. Paper 2 provides valuable empirical insights about LLM behavior in code optimization but is more diagnostic/analytical in scope, focused on a narrower domain (hardware-aware optimization). Paper 1's contribution is more actionable, broadly applicable, and opens a new research direction (action representation learning for agents), giving it higher potential impact.
Paper 1 likely has higher impact: it introduces a novel control-theoretic/robotics framing for LLM guardrails that targets trajectory-level safety with enforceable runtime constraints, addressing a timely, high-stakes gap in socially sensitive deployments. Its real-world application domains (education, mental health, caregiving) broaden societal and interdisciplinary impact (AI safety, HRI, control, ML, social sciences). Although Paper 2 is technically strong and useful for efficiency, it is more incremental within agent optimization and likely narrower in cross-field and societal reach.
Paper 2 is likely higher impact because it introduces a broadly applicable, conceptually novel reparameterization of the agent action space (learned latent actions) that can reduce decision horizon and inference cost across many agent settings and benchmarks. This targets a central scalability bottleneck for LLM agents and should transfer across domains, tasks, and model families, potentially influencing both research on hierarchical/latent control and practical deployment. Paper 1 is valuable and rigorous for long-horizon scientific workflows, but is more domain-specific (memory consolidation for scientific agents) and closer to engineering a specialized architecture.
Paper 1 addresses a fundamental and broadly applicable bottleneck in LLM agent systems—action space representation—which affects the entire growing field of LLM agents. Its framework (LAR) introduces a novel conceptual contribution (latent action reparameterization) that is complementary to existing optimizations and applicable across diverse agent benchmarks. Paper 2 makes a solid but narrower contribution, improving credit assignment for generative recommendation via step-aligned advantages. While technically sound, its impact is confined to the recommendation domain. Paper 1's broader applicability, novelty in reframing agent efficiency as an action representation problem, and relevance to scaling LLM agents give it higher potential impact.
Paper 1 likely has higher impact due to greater novelty (learned latent action reparameterization for LLM agents), broad applicability across many agent tasks, and strong timeliness given the current focus on reducing LLM inference cost. Its approach could generalize to multiple domains (planning, RL, systems/efficiency) and influence how agent action spaces are designed. Paper 2 addresses an important clinical problem, but the methodology (feature extraction + standard regression on modest, imbalanced datasets) appears more incremental and narrower in scope, with impact more confined to TCD-based vascular aging studies.
SaaS-Bench addresses a critical gap in evaluating computer-use agents on realistic professional workflows, providing a concrete benchmark across 23 real SaaS systems with 106 tasks. The finding that even the best models complete fewer than 4% of tasks reveals a stark capability gap that will drive significant future research. While LAR's latent action reparameterization is a solid methodological contribution to inference efficiency, SaaS-Bench has broader impact potential: it defines a new evaluation paradigm for the rapidly growing CUA field, will likely be widely adopted as a standard benchmark, and its cross-domain coverage invites contributions from multiple research communities.
Paper 2 addresses a highly timely and practically important problem—improving LLM agent efficiency—in a rapidly growing field. Its approach of learning compact latent action spaces is novel, broadly applicable across LLM agent benchmarks, and complementary to existing optimization strategies. The potential for real-world impact is significant given the widespread deployment of LLM agents. Paper 1, while technically solid in advancing belief function theory, operates in a more niche domain (Dempster-Shafer theory/evidential reasoning) with a narrower audience and less immediate broad impact.