Agents That Learn by Doing: Closing the Feedback Loop

Author: Denis Avetisyan

Researchers have developed an AI model that allows agents to independently assess their actions by actively probing the environment and interpreting the resulting changes.

The proposed Actively Feedback Getting model establishes a framework for iterative refinement, enabling a system to progressively approach a desired state through continuous feedback and adjustment-a process fundamentally governed by the principle that minimizing the error function <span class="katex-eq" data-katex-display="false"> E = \sum_{i=1}^{n} |y_i - \hat{y}_i| </span> yields optimal performance. — The proposed Actively Feedback Getting model establishes a framework for iterative refinement, enabling a system to progressively approach a desired state through continuous feedback and adjustment-a process fundamentally governed by the principle that minimizing the error function $E = \sum_{i=1}^{n} |y_i - \hat{y}_i|$ yields optimal performance.

This work introduces a novel approach to autonomous action evaluation that bypasses the need for predefined metrics by leveraging active feedback acquisition and difference-based detection.

Intelligent agents often struggle to evaluate actions in novel environments lacking pre-defined success metrics. This limitation motivates the research presented in ‘Actively Obtaining Environmental Feedback for Autonomous Action Evaluation Without Predefined Measurements’, which introduces a model enabling agents to proactively seek and validate feedback through environmental interaction. By exploiting action-induced changes, the proposed system autonomously discovers relevant feedback signals without relying on external commands or pre-specified measurements. Could this approach unlock truly adaptable intelligence capable of learning and operating effectively in entirely unknown contexts?

The Challenge of Autonomous Feedback: A Matter of Algorithmic Purity

Conventional reinforcement learning systems typically depend on meticulously designed reward signals to guide an agent’s behavior, but this approach presents significant limitations when applied to dynamic, real-world scenarios. These predefined rewards, while effective in simple environments, often fail to capture the nuances of complex tasks, hindering an agent’s ability to adapt to unforeseen circumstances or optimize for long-term goals. The rigidity of these systems means that even slight deviations from the expected conditions can lead to suboptimal performance, as the agent is essentially ‘locked’ into pursuing a narrowly defined objective. Consequently, a core challenge lies in developing agents capable of thriving not through explicit instruction, but through a more flexible and intrinsic understanding of their environment and the consequences of their actions.

The pursuit of truly autonomous agents encounters significant hurdles when considering the necessity of effective learning strategies. Current systems frequently demonstrate limited capacity to acquire skills or adapt to novel situations without consistent, explicit guidance – often in the form of labeled data or predefined reward functions. This reliance on external direction fundamentally restricts an agent’s ability to operate independently and generalize its knowledge to unforeseen circumstances. Consequently, progress toward genuine autonomy necessitates innovative approaches that allow agents to internally assess performance, identify areas for improvement, and refine their behavior without constant external supervision; a capacity mirroring the intrinsic motivation and self-correction observed in biological intelligence.

A fundamental bottleneck in achieving truly autonomous artificial intelligence lies in the difficulty agents face when discerning beneficial actions without reliance on externally provided feedback. Current systems predominantly depend on pre-programmed reward signals, which necessitate human definition of success and severely limit adaptability to unforeseen circumstances. This reliance creates a dependency that undermines genuine autonomy; an agent cannot independently learn and improve if it requires constant instruction regarding what constitutes progress. The challenge, therefore, isn’t simply optimizing for a known reward, but enabling the agent to internally generate signals indicating favorable or unfavorable outcomes – effectively allowing it to learn from the consequences of its actions and, crucially, to define ‘good’ for itself. Without this capacity for self-assessment, agents remain tethered to human expectations, hindering their ability to navigate novel situations and achieve true independence.

Active Feedback Acquisition: Shifting the Paradigm

The Actively Feedback Getting Model represents a shift in agent design, moving beyond passive reception of feedback to proactive generation of evaluative signals. This agent is characterized by its capacity for autonomous action, specifically undertaking deliberate interventions within its environment. Unlike traditional reinforcement learning paradigms reliant on externally provided rewards, this model operates by initiating changes and then interpreting the consequences as feedback. This capability allows the agent to explore its environment and learn without pre-defined reward functions or human oversight, effectively creating its own learning signal through action and observation.

Active Action Intervention within the agent’s feedback loop involves purposeful manipulation of the environment to generate observable changes. This is achieved through discrete actions designed to alter specific parameters or states within the operating context. The agent does not passively await external signals; instead, it actively initiates changes and then monitors the resulting consequences. These interventions are not random; they are formulated based on the agent’s internal model and hypotheses about cause-and-effect relationships. The magnitude and type of environmental change induced by each action are recorded, forming the basis for subsequent feedback analysis and model refinement.

Difference-Driven Feedback Detection operates by monitoring the environment for alterations resulting directly from the agent’s actions; this ‘Action-Induced Change’ constitutes the feedback signal. The system doesn’t rely on externally provided labels or rewards, but instead identifies differences between pre-action and post-action states. These differences are quantified and analyzed to determine the impact of a given action, effectively allowing the agent to self-assess performance. The magnitude and nature of the change are crucial; significant deviations from the expected baseline indicate a stronger feedback signal, while negligible changes suggest minimal impact. This method allows the agent to iteratively refine its understanding of the environment and its own capabilities without requiring pre-defined reward structures.

Unlike traditional reinforcement learning paradigms that rely on externally provided reward signals, the agent operates on the principle of self-generated evaluative signals. It doesn’t passively await feedback; instead, it actively intervenes in its environment through deliberate actions. These ‘Active Action Interventions’ are designed to induce observable changes, and it is the detection of these ‘Action-Induced Changes’ – the differences between states before and after intervention – that constitute the feedback. Consequently, the agent constructs the conditions necessary for feedback to emerge as a direct result of its own actions, effectively shifting from a recipient to a generator of evaluative information.

Building a Memory of Action and Outcome: A Foundation for Learning

Accumulated Learning functions by continuously recording the relationship between actions taken by the agent and the resulting feedback received from the environment. This is not a static dataset; instead, it’s a dynamically updated knowledge base where each action-feedback pair is stored as an experience. The system prioritizes retaining this data to allow for future generalization and adaptation to novel situations. Unlike traditional methods that might rely on pre-defined rules or fixed value functions, Accumulated Learning enables the agent to build its understanding of the environment through repeated interaction and observation, effectively creating a history of successful and unsuccessful strategies. The size and complexity of this knowledge base grow with the agent’s experience, contributing to increasingly sophisticated behavior over time.

Difference-Centered Memory functions by prioritizing the storage of action-outcome relationships determined to be most impactful to the agent’s environment. Rather than recording all observed transitions with equal weight, the system identifies and retains data associated with substantial changes in state. This is achieved through a calculation of the difference between the pre-action and post-action environmental states; larger differences indicate more significant events. The magnitude of this difference serves as a weighting factor, with relationships tied to larger changes being stored with higher priority and potentially greater recall accuracy. This selective storage optimizes memory usage and focuses learning on the stimuli that demonstrably alter the agent’s surroundings, improving efficiency in complex environments.

Obvious Recording is a mechanism designed to address learning challenges in environments where feedback is infrequent but impactful. This process explicitly stores action-outcome relationships specifically when the received feedback deviates significantly from expectations, or represents a novel state. Unlike standard learning methods that may require numerous iterations to detect subtle changes, Obvious Recording prioritizes the capture of these critical, yet rare, events. This is achieved by maintaining a higher sensitivity to unusual outcomes, ensuring that even infrequent positive or negative reinforcement signals are reliably recorded and incorporated into the agent’s accumulated knowledge base, thus accelerating learning in sparse reward scenarios.

Unlike traditional reinforcement learning which passively receives data from the environment, this system incorporates elements of active learning by strategically selecting actions designed to maximize information gain. This is achieved by prioritizing exploration of scenarios where the agent is uncertain about the relationship between actions and their outcomes; rather than random exploration, the agent actively seeks states that will reduce this uncertainty most efficiently. This targeted exploration allows the system to learn more rapidly and effectively, particularly in complex environments where random sampling would be inefficient. The system identifies informative scenarios by assessing the potential impact of an action on its internal model of the environment, allowing it to proactively gather data that enhances its knowledge base.

Across multiple trials, the active querying method demonstrates superior efficiency in information gathering compared to passive observation.

Reasoning and Discovery Through Intervention: Beyond Correlation

The framework’s core functionality relies on sophisticated Large Language Model (LLM) reasoning, specifically harnessing the capabilities of models such as DeepSeek-70B. This isn’t merely about processing data; the agent actively plans interventions – deliberate actions designed to probe the system and reveal underlying causal relationships. Following each intervention, the LLM interprets the resulting changes, drawing inferences about how different elements interact. This active, reasoning-driven approach allows the agent to go beyond simple observation, constructing a dynamic understanding of the system through a cycle of action and analysis. By strategically manipulating variables and carefully evaluating the outcomes, the agent can effectively map the complex web of connections that govern the environment, surpassing the limitations of purely passive learning methods.

Traditional machine learning often relies on passive observation of data, identifying correlations but struggling to establish true cause-and-effect relationships. This framework, however, actively engages with the system under study through deliberate interventions – carefully planned manipulations of variables. By observing the resulting changes, the agent doesn’t simply detect patterns; it tests hypotheses about how different elements influence one another. This approach to causal discovery surpasses the limitations of passive learning by enabling the agent to move beyond correlation and towards understanding the underlying mechanisms driving observed phenomena, ultimately building a more robust and actionable model of the world.

The framework’s capacity for counterfactual analysis represents a significant advancement in understanding complex systems. By simulating alternative actions and their potential outcomes, the system moves beyond simply observing what did happen to evaluating what could have happened. This is achieved through targeted queries that explore “what if” scenarios, allowing the agent to assess the causal impact of specific interventions. For instance, the system can determine not only that a particular action led to a specific result, but also what the outcome would likely have been had a different course of action been taken. This capability is crucial for optimizing strategies, identifying unintended consequences, and building more robust and reliable models of causality, ultimately enabling proactive decision-making rather than reactive responses.

The framework demonstrates a marked efficiency in knowledge acquisition, requiring an average of just 2.952 queries to a large language model to reach a conclusion. This represents a substantial reduction compared to passive observational methods, which necessitate an average of 5.286 queries to achieve the same level of understanding. Crucially, this difference isn’t merely a trend; statistical analysis confirms the superiority of this active intervention approach with a p-value of 0.0216, indicating a high degree of confidence in the results. This reduced reliance on LLM queries not only streamlines the discovery process but also suggests potential cost savings and faster insights when employing these advanced reasoning tools.

Beyond simply requiring fewer queries, the intervention-based approach exhibits markedly more consistent performance than passive observation. Analysis reveals a standard deviation of just 1.359 LLM queries needed to reach a conclusion, a substantial improvement over the 4.137 observed with methods relying solely on observation. This lower standard deviation indicates that the system’s reasoning process is less variable; it consistently arrives at answers with a similar number of steps, regardless of the specific scenario. Such stability is crucial for reliable application, suggesting the framework isn’t prone to unpredictable spikes in computational cost or reasoning complexity, and offers a more predictable and efficient path to discovery.

Evaluations reveal a distinct advantage for a reasoning strategy centered on identifying differences rather than directly assessing outcomes. This approach, which focuses on pinpointing the specific changes resulting from interventions, achieves a semantic similarity score of 0.3659 – a notable improvement over the 0.2918 score attained by a direct reasoning strategy. This indicates the difference-oriented method is more effective at capturing the nuanced relationships between actions and their consequences, allowing for a more precise understanding of the underlying causal mechanisms at play. The increased semantic similarity suggests the agent can more accurately interpret the results of its interventions, leading to more robust causal discovery and informed decision-making.

Difference-oriented reasoning consistently achieves higher semantic similarity to reference causal explanations than direct reasoning.

Toward Intrinsic Motivation and Autonomous Exploration: The Path Forward

The emergence of intrinsic motivation in artificial agents hinges on connecting internally generated impulses – termed ‘Internal Action Triggers’ – with a dedicated process of seeking and interpreting feedback. Rather than relying solely on external rewards to guide behavior, this framework proposes that agents are driven to act based on internally generated curiosity or a desire for novelty. By linking these internal triggers to feedback mechanisms, the agent actively evaluates the consequences of its actions, reinforcing behaviors that lead to predictable or informative outcomes. This cycle fosters a self-sustaining learning process, enabling continuous improvement and adaptation without the need for constant external direction, and laying the groundwork for truly autonomous exploration and problem-solving.

The capacity for agents to pursue learning independently of external rewards represents a significant advancement in artificial intelligence. This intrinsic motivation fosters a cycle of continuous improvement, allowing the agent to actively seek out novel experiences and refine its understanding of the environment. Rather than relying solely on predefined goals or externally provided feedback, the agent is driven by an internal desire to explore and master its surroundings, leading to more robust and adaptable behavior. This self-directed learning not only enhances performance in existing tasks but also equips the agent to tackle unforeseen challenges and discover innovative solutions – a crucial step toward truly intelligent systems capable of operating effectively in complex, real-world scenarios.

The architecture underpinning this system isn’t confined to static simulations; its inherent adaptability allows for robust performance even within complex, dynamic environments. Unlike many reinforcement learning approaches brittle to unexpected changes, this framework continually refines its internal models through ongoing feedback-seeking and action triggering. This means an agent can navigate previously unseen obstacles, adjust to shifting goalposts, or even learn from inconsistent data streams without catastrophic failure. The system’s ability to internally generate curiosity and prioritize information based on predictive error-rather than relying solely on pre-programmed responses-facilitates a continuous learning cycle crucial for real-world application in areas like robotics, autonomous navigation, and adaptive control systems where environments are rarely predictable or stable.

The current framework, while demonstrating promising results in controlled settings, is poised for significant expansion through several avenues of future research. Investigations will center on scaling these internally-driven exploration techniques to address more complex and dynamic environments, necessitating advancements in computational efficiency and algorithmic robustness. A key goal involves integrating this intrinsic motivation system with broader artificial intelligence architectures – including those focused on reinforcement learning and world modeling – to create agents capable of not only adapting to change but proactively seeking knowledge and refining their skillsets independently. Such integration promises to move beyond task-specific learning towards the development of truly autonomous and continually improving AI systems, potentially unlocking solutions to problems requiring long-term adaptation and unforeseen circumstances.

The pursuit of autonomous action, as detailed in this research, hinges on an agent’s ability to discern meaningful change-to actively seek validation of its interactions with the environment. This echoes Marvin Minsky’s assertion: “The more we understand about how things work, the more we realize how little we understand.” The model presented doesn’t rely on pre-defined metrics, instead formulating its own understanding through manipulation and observation. It’s a process of revealing invariants, of moving beyond simply achieving a task to understanding why an action yields a particular result. If the agent’s causal reasoning feels opaque, the invariant hasn’t been fully revealed; the system needs further probing to establish a provable understanding of the action-feedback relationship.

Beyond Signals: Charting the Course

The pursuit of autonomous action, as demonstrated by this work, reveals a fundamental tension. While algorithms can efficiently map inputs to outputs, true intelligence necessitates a robust understanding of consequence – a demonstrable link between action and demonstrable change. The elegance of this approach lies in its rejection of pre-defined metrics; the agent doesn’t seek confirmation of a pre-existing reward, but actively creates the conditions for validation. However, this introduces a new layer of complexity. Establishing causality from observed differences requires more than statistical correlation; it demands a formalization of expectation – a predictive model of the environment capable of distinguishing genuine consequence from random fluctuation.

Future iterations must address the inherent limitations of difference-based detection. Noise, even in controlled environments, will inevitably confound the signal. The current framework, while conceptually sound, lacks a formal mechanism for quantifying uncertainty and managing the trade-off between exploration and exploitation. A rigorous mathematical treatment of these uncertainties – perhaps drawing upon Bayesian frameworks or information-theoretic principles – will be crucial to achieving truly reliable autonomous action.

Ultimately, the question isn’t simply whether an agent can react to its environment, but whether it can understand it. This work represents a step towards that goal, but the path forward demands a renewed focus on provable consistency – on algorithms that are not merely effective, but demonstrably correct, even in the face of inherent complexity.

Original article: https://arxiv.org/pdf/2601.04235.pdf

Contact the author: https://www.linkedin.com/in/avetisyan/