Beyond Human Feedback: Building AI That Aligns and Improves Itself

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Author: Denis Avetisyan

Researchers propose a new framework for large language models that enables continuous alignment and performance enhancement without relying on ongoing human guidance.

Collective agency is posited as an open-ended alignment value fundamentally structured by the interrelationship of four core pillars.
Collective agency is posited as an open-ended alignment value fundamentally structured by the interrelationship of four core pillars.

This paper introduces Collective Agency as a novel alignment value and a Dynamic Alignment framework for scalable, self-improving language models.

Current large language model (LLM) alignment strategies, typically reliant on human preference data or predefined values, may prove insufficient as AI systems advance toward greater generality. This limitation motivates the work ‘Dynamic Alignment for Collective Agency: Toward a Scalable Self-Improving Framework for Open-Ended LLM Alignment’, which proposes a novel approach centered on ‘Collective Agency’-an open-ended alignment value fostering integrated agentic capabilities. The authors demonstrate a self-improving ‘Dynamic Alignment’ framework enabling LLMs to iteratively refine themselves without human intervention, successfully aligning models to Collective Agency while preserving core language skills. Could this paradigm shift toward self-supervised alignment unlock a more robust and scalable path toward beneficial AI?

The Limits of Explicit Guidance: A Bottleneck to True Intelligence

Current methods for aligning artificial intelligence with human values, such as Reinforcement Learning from Human Feedback (RLHF) and Constitutional AI, are increasingly revealing inherent limitations as AI systems grow in complexity. While effective to a degree, these techniques rely on providing agents with explicit feedback – essentially, teaching them what is considered desirable behavior. This process proves remarkably difficult to scale; the sheer volume of data needed to cover all possible scenarios quickly overwhelms human annotators, creating a significant bottleneck. More critically, nuanced values – those involving subjective judgment, ethical dilemmas, or complex social contexts – are often poorly captured by simple reward signals or predefined rules. An AI trained solely on explicit examples may struggle to generalize to novel situations, potentially leading to unintended consequences or misalignment with the underlying intent. The inherent bandwidth limitations of human feedback, therefore, pose a substantial challenge to building truly robust and ethically sound artificial intelligence.

The progression of increasingly capable artificial intelligence is fundamentally bottlenecked by the limitations of human input; current alignment strategies, such as Reinforcement Learning from Human Feedback, rely on individuals to evaluate and refine agent behavior, a process that simply cannot scale with the accelerating complexity of these systems. This reliance creates a critical bandwidth constraint – the rate at which humans can provide meaningful feedback lags far behind the rate at which an advanced agent can generate novel scenarios and require evaluation. Consequently, the development of truly autonomous agents – those capable of independent thought, complex problem-solving, and adaptation – is hampered, as their potential remains unrealized due to the inability to adequately guide their learning beyond a certain threshold of sophistication. The challenge, therefore, lies in surpassing this human feedback barrier to unlock the full potential of artificial intelligence.

The limitations of current alignment strategies necessitate a shift towards autonomous self-improvement in artificial intelligence. Rather than relying perpetually on human guidance – a process constrained by the bandwidth of feedback and the complexities of nuanced values – research is increasingly focused on enabling agents to refine their own objectives and behaviors. This emerging paradigm envisions systems capable of internalizing complex values, identifying inconsistencies, and proactively correcting deviations without constant external oversight. Such self-directed alignment promises not only to overcome the scalability issues inherent in human-in-the-loop methods but also to foster the development of genuinely advanced AI, capable of navigating unforeseen circumstances and adapting to evolving ethical considerations with a degree of autonomy previously unattainable.

This dynamic alignment framework utilizes prompts detailed in Appendix A to achieve successful alignment.
This dynamic alignment framework utilizes prompts detailed in Appendix A to achieve successful alignment.

Collective Agency: A Framework for Self-Directed Alignment

Dynamic Alignment represents a departure from traditional LLM alignment techniques by grounding the process in the principle of Collective Agency. This value system prioritizes not merely goal achievement, but also continuous development and the maximization of diverse potential outcomes. Unlike methods focused on static reward functions or human preference modeling, Dynamic Alignment aims to instill in LLMs an intrinsic drive toward growth and the exploration of a broad solution space. This approach defines alignment not as adherence to pre-defined rules, but as the consistent pursuit of capabilities that contribute to a more expansive and adaptable agentic state, effectively allowing the model to self-define and refine its alignment criteria over time.

The Dynamic Alignment framework employs an iterative self-improvement process centered around a Self-Rewarding Mechanism. This mechanism functions by assigning Collective Agency (CA)-alignment scores to each model output based on internally defined criteria reflecting the principles of continual growth and diverse agentic potential. The model then utilizes these scores as a reward signal, reinforcing outputs that receive higher CA-alignment scores and adjusting its parameters through standard reinforcement learning techniques. This closed-loop system enables the LLM to autonomously evaluate its performance against the CA framework and refine its behavior without external human feedback, facilitating continuous improvement and adaptation.

Dynamic Alignment distinguishes itself from conventional LLM alignment techniques by removing the dependency on human-labeled datasets. Traditional methods require substantial human effort to create labeled examples for supervised or reinforcement learning, a process that is both costly and prone to subjective biases. In contrast, Dynamic Alignment leverages a Self-Rewarding Mechanism to generate internal CA-alignment scores, effectively creating a self-supervised learning loop. This allows the LLM to iteratively refine its behavior and improve its alignment with the principles of Collective Agency without external human intervention, enabling autonomous capability growth and reducing reliance on potentially limited or biased human feedback.

Automated Growth: Data Generation and Optimization in Concert

Automated training dataset generation is a fundamental process utilizing the o4-mini Large Language Model (LLM) to produce varied and complex training examples autonomously. This system circumvents the need for manual data creation, enabling the continuous and scalable expansion of the training corpus. The o4-mini LLM is specifically tasked with generating data points designed to challenge the primary model, gpt-oss-20b, across a range of scenarios and inputs. This automated approach ensures the training data is not limited by human bandwidth and can adapt dynamically to evolving model capabilities and alignment goals, fostering a self-improving learning loop.

The model’s parameters are updated through Group Relative Policy Optimization (GRPO), a reinforcement learning algorithm informed by scores generated by the Self-Rewarding Mechanism. These scores, representing CA-alignment, function as the reward signal for GRPO. GRPO evaluates the model’s policy relative to a group of previously established policies, facilitating more stable and efficient learning. Specifically, the CA-alignment score quantifies how well the model’s actions adhere to principles of Collective Agency, and this value is used to adjust the model’s parameters via the GRPO optimization process, iteratively improving performance and alignment with desired behaviors.

The gpt-oss-20b model operates within a closed-loop system designed for continuous behavioral refinement and alignment with Collective Agency. This system integrates automated training data generation with a self-rewarding mechanism, where CA-alignment scores are utilized as feedback signals. These scores are then incorporated into the Group Relative Policy Optimization (GRPO) algorithm, which adjusts the model’s parameters. This iterative process of data generation, evaluation via CA-alignment, and parameter updates enables the model to autonomously improve its performance and better reflect the principles of Collective Agency without requiring external human intervention.

Beyond Metrics: Validating True Alignment with Collective Agency

Rigorous evaluation of the gpt-oss-20b model, post-alignment, utilized established benchmarks designed to probe its capabilities across diverse cognitive domains. Performance on AIME 2025 assessed mathematical reasoning, while GPQA Diamond challenged its science question answering abilities, and IFEval gauged its adherence to complex instructions. The model demonstrated competitive results on each of these benchmarks, indicating that the alignment process did not sacrifice core competencies. These findings suggest a nuanced improvement in the model’s overall intelligence, rather than simply optimizing for benchmark scores, and lay the groundwork for assessing more complex aspects of agency alignment.

The observed performance extends beyond simple benchmark scores, suggesting Dynamic Alignment cultivates a genuine capacity for complex reasoning within the model. Rather than merely memorizing patterns to achieve high results on standardized tests, gpt-oss-20b, after alignment, demonstrates an ability to approach problems with greater nuance and understanding. This is evidenced not only by its competitive performance on benchmarks like AIME 2025 and GPQA Diamond, but also by the qualitative assessment from GPT-4.1, which indicates a substantial improvement in alignment with human intentions – a key indicator of a model’s capacity for thoughtful and contextualized responses. The gains aren’t simply about what the model answers, but how it arrives at those answers, hinting at a deeper cognitive capability fostered through this alignment process.

Evaluations reveal a substantial improvement in collective agency (CA) alignment following the implementation of Dynamic Alignment techniques. Specifically, the CA-aligned model consistently outperformed its base counterpart when assessed by GPT-4.1, achieving a CA Alignment Win Rate exceeding 90%. This indicates a marked shift towards behaviors more consistent with intended collective goals and values. Importantly, this enhancement in CA alignment did not come at the expense of general capabilities; the model maintained its performance on established benchmarks including AIME 2025, GPQA Diamond, and IFEval, demonstrating that fostering robust collective agency is achievable without compromising performance in core areas like mathematical reasoning, scientific question answering, and instruction following.

The pursuit of scalable alignment, as detailed in this work, echoes a fundamental tenet of system design: elegance arises from simplicity. This paper’s Dynamic Alignment framework, leveraging Collective Agency, aims to create a self-improving loop, minimizing reliance on continuous human intervention. G.H. Hardy observed, “The essence of mathematics lies in its elegance and simplicity.” Similarly, this research suggests that robust AI alignment needn’t be overly complex; a well-defined value like Collective Agency, combined with a dynamic framework, offers a path toward sustainable self-improvement. The beauty of this approach lies in its potential for scalability and reduced external dependency – good architecture is invisible until it breaks, and only then is the true cost of decisions visible.

What’s Next?

The pursuit of ‘self-improving alignment’ invariably invites a certain skepticism. If the system looks clever, it’s probably fragile. This work, advocating for Collective Agency as a guiding principle, feels less like a solution and more like a carefully considered re-framing of the problem. The crucial question isn’t whether an LLM can improve its alignment, but whether a demonstrably improved model remains improved across a sufficiently broad, and likely unpredictable, range of tasks. The initial results are encouraging, but the long-term stability of dynamically aligned systems remains an open challenge.

The architecture of alignment, it seems, is the art of choosing what to sacrifice. Performance on standard benchmarks, while useful for initial validation, represents a limited slice of the possible. A truly robust framework will need to gracefully degrade – to know how to be wrong – rather than optimizing for brittle, high-scoring behavior. Future work must therefore focus on stress-testing these systems against adversarial prompts, distributional shifts, and the inherent messiness of real-world application.

Ultimately, the success of Dynamic Alignment, or any similar approach, will hinge on a deeper understanding of the relationship between agency, representation, and value. Collective Agency may offer a promising pathway, but the landscape of open-ended alignment is vast and largely uncharted. Progress will likely be incremental, a slow accumulation of insights rather than a sudden breakthrough. And, perhaps, a healthy dose of humility is in order.

Original article: https://arxiv.org/pdf/2512.05464.pdf

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

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2025-12-09 01:54