Learning by Doing: How Agents Can Reason Through Action

Author: Denis Avetisyan

A new training pipeline leverages reinforcement learning to enable conversational agents to improve their reasoning abilities by actively interacting with their environment.

The training process exhibits reward curves indicative of successful learning, demonstrating the algorithm's convergence towards an optimal policy. — The training process exhibits reward curves indicative of successful learning, demonstrating the algorithm’s convergence towards an optimal policy.

This work presents a three-stage reinforcement learning approach, utilizing Group Relative Policy Optimization, to enhance reasoning and tool use in conversational agents with minimal reliance on annotated reasoning data.

While supervised fine-tuning has proven effective for large language models, generalization remains a challenge when faced with shifting data distributions. This limitation motivates the work ‘When Actions Teach You to Think: Reasoning-Action Synergy via Reinforcement Learning in Conversational Agents’, which proposes a novel reinforcement learning pipeline to directly optimize reasoning and action-taking in conversational agents. By iteratively refining a model’s ability to both reason through problems and utilize tools, this approach achieves significant gains in reasoning quality and tool invocation accuracy-a 40% improvement over a baseline model-with minimal reliance on costly reasoning annotations. Could this synergy between reasoning and action learning unlock a new generation of more capable and adaptable conversational AI?

Establishing Conversational Foundations: Priming Language Models for Dialogue

Large Language Models (LLMs) currently underpin the vast majority of advanced conversational AI systems, yet their impressive abilities aren’t simply ‘out-of-the-box’. These models, often possessing billions of parameters, begin as powerful but generalized text predictors; transitioning them into coherent and engaging conversationalists demands a meticulous initialization process. Initial performance is heavily reliant on the quality and structure of the data used to ‘prime’ the LLM; poorly curated datasets can lead to biased responses, factual inaccuracies, or simply an inability to maintain a consistent conversational flow. Consequently, significant research focuses on developing effective strategies for pre-training and fine-tuning, establishing a strong foundation upon which more complex conversational skills – such as nuanced understanding and creative generation – can be built. The initial configuration isn’t merely a starting point, but a critical determinant of the model’s ultimate conversational competence and reliability.

The foundation of any sophisticated conversational AI lies in its initial training, and a crucial step is Supervised Fine-Tuning (SFT). This process takes a pre-trained Large Language Model and adapts it specifically for dialogue by learning from curated datasets like APIGen-MT-5k. This dataset, consisting of paired prompts and desired responses, allows the model to grasp fundamental conversational abilities – understanding user intent, formulating coherent replies, and maintaining a basic conversational flow. Through exposure to these examples, the model learns to predict appropriate responses, establishing a baseline level of competence before more advanced training techniques are applied. Without this initial SFT, the model would lack the essential skills needed to engage in meaningful dialogue, remaining a powerful text generator but not a true conversational partner.

Adapting large language models to specific conversational tasks traditionally demanded updating billions of parameters, a computationally expensive undertaking. However, parameter-efficient techniques, such as Low-Rank Adaptation (LoRA), offer a compelling alternative. LoRA freezes the pre-trained model weights and introduces a smaller set of trainable parameters – low-rank matrices – that capture task-specific nuances. This drastically reduces the number of parameters needing adjustment, often by orders of magnitude, while maintaining comparable performance to full fine-tuning. Consequently, LoRA enables researchers and developers to customize these powerful models on limited hardware, facilitating wider accessibility and accelerating the development of specialized conversational AI applications. The technique not only lowers computational costs but also reduces storage requirements, as only the smaller, adapted parameters need to be saved and deployed.

Reinforcement learning improves upon a cold-start model to generate more effective results.

Imbuing Structured Thought: A Foundation for Reasoning in LLMs

Cold-Start Supervised Fine-Tuning (SFT) leverages a small, specifically annotated dataset to initiate the development of structured reasoning capabilities in Large Language Models (LLMs) following an initial, broader fine-tuning phase. This process doesn’t require extensive data; the annotated dataset focuses on examples demonstrating the desired reasoning process, such as step-by-step problem-solving or logical inference. The aim is to ‘prime’ the LLM with the format of structured thought, allowing it to generalize this format to unseen problems. This contrasts with pre-training or initial fine-tuning, which primarily focus on language modeling and general knowledge acquisition; Cold-Start SFT specifically targets the application of that knowledge in a reasoned manner.

Low-Rank Adaptation (LoRA) facilitates efficient parameter adaptation during the structured reasoning phase of Large Language Model (LLM) training by freezing the pre-trained model weights and introducing trainable low-rank decomposition matrices. This approach significantly reduces the number of trainable parameters – often by over 90% – compared to full fine-tuning, decreasing computational costs and memory requirements. Specifically, LoRA approximates weight updates with a low-rank matrix factorization, effectively learning only the changes needed for the new task without modifying the original, extensive parameter set. This maintains computational feasibility, allowing for iterative refinement of reasoning abilities without prohibitive resource demands, and enables experimentation with smaller datasets.

The initial reasoning scaffold, established through techniques like Cold-Start SFT, serves as a crucial base for subsequent model improvement. This foundation allows for focused optimization through methods such as reinforcement learning or expanded fine-tuning datasets, enabling the LLM to refine its reasoning capabilities beyond the initial structured examples. Further refinement can address limitations in the initial scaffold, such as coverage of diverse reasoning patterns or handling of ambiguous inputs, leading to a more robust and generalized reasoning engine. The efficiency gained from using LoRA during scaffold creation facilitates these iterative refinement cycles without incurring prohibitive computational costs.

Optimizing Reasoning and Action: A Reinforcement Learning Paradigm

Reinforcement Learning (RL) is utilized to simultaneously enhance both the quality of reasoning exhibited by the model and its performance on downstream tasks. This joint optimization contrasts with traditional approaches that treat reasoning and task completion as separate objectives. By framing the problem as an RL task, the model learns a policy that maximizes cumulative rewards derived from both reasoning accuracy and task success. This allows for a more holistic training process where improvements in reasoning directly contribute to better task performance, and vice-versa, leading to a more robust and capable system. The RL framework enables the model to explore different reasoning pathways and identify those that yield the highest combined reward, effectively balancing reasoning depth with practical applicability.

The reinforcement learning process is guided by quantifiable rewards designed to improve both the correctness and efficiency of the model’s reasoning. Specifically, the Conditional Accuracy Reward measures the accuracy of each reasoning step given the preceding steps and the final answer, providing a signal for improved logical flow. Complementing this, the Thinking Length Reward incentivizes concise reasoning by penalizing unnecessary or redundant steps; this reward is typically inversely proportional to the number of reasoning steps taken to reach a solution. These rewards, when combined, encourage the model to produce accurate results with minimal computational expense and a streamlined reasoning process.

Format Compliance Reward is a component of the reinforcement learning framework designed to incentivize adherence to a predefined output structure. This reward signal is calculated based on whether the generated response conforms to specific formatting rules, such as the presence of required fields, correct ordering of information, or adherence to a designated schema. A higher reward is assigned for outputs that strictly comply with these rules, while non-compliant outputs receive a lower or zero reward. This mechanism directly improves the usability of the model’s responses by ensuring consistent and predictable output formats, simplifying downstream processing and integration with other systems.

Group Relative Policy Optimization (GRPO) addresses the challenges of reinforcement learning in complex reward landscapes by framing the policy optimization problem as a relative comparison between a group of policies. Instead of directly maximizing absolute reward, GRPO aims to improve the policy’s rank within the group, simplifying the learning signal and enhancing stability. This is achieved by estimating the advantage of a policy relative to others in the group, allowing for more efficient exploration and exploitation. The algorithm utilizes a weighted sampling approach to approximate the relative policy performance, reducing variance and enabling effective learning even with noisy or sparse rewards. GRPO’s formulation is particularly suited to scenarios where defining a precise absolute reward function is difficult, but relative comparisons are straightforward, as is the case when evaluating reasoning quality and task performance in language models.

Evaluating Generalization and Robustness: Beyond the Confines of Training

The model’s capacity to perform effectively in real-world applications hinges on its ability to generalize beyond the data it was initially trained on; this was rigorously tested using the out-of-domain dataset, Almita. This dataset presents scenarios and complexities not explicitly encountered during training, serving as a crucial benchmark for evaluating the model’s adaptability. Performance on Almita indicates how well the model can extrapolate learned patterns to novel situations, revealing its robustness and potential for reliable operation in unpredictable environments. Successful performance on this challenging dataset demonstrates a significant step towards creating AI systems capable of navigating the complexities of the real world, rather than being limited to the confines of their training data.

Evaluating a model’s proficiency in utilizing external tools, such as APIs, requires specific assessment metrics beyond standard language modeling benchmarks. Action Classification measures the model’s ability to correctly identify the appropriate action to take given a user request, while Tool Call Accuracy gauges whether the model can successfully invoke the correct API with the necessary parameters. These metrics provide a granular understanding of performance on API interactions, pinpointing whether failures stem from incorrect action selection or improper tool usage. A high score in both areas indicates the model not only understands the user’s intent but can also reliably translate that understanding into functional API calls, crucial for building truly helpful and automated systems.

Assessing the semantic correctness of generated responses requires more than simply checking for keyword matches; it demands an evaluation of whether the model truly understands the query and provides a logically sound answer. To this end, Cross-Encoder Similarity is employed, a technique that feeds both the query and the generated response into a shared neural network. This allows the model to assess the relationship between the two texts, producing a similarity score that reflects their semantic relatedness. Unlike methods that evaluate sentence fragments independently, Cross-Encoders consider the entire context of both the query and response, providing a more nuanced and accurate measure of semantic correctness. A higher score indicates a stronger semantic connection, suggesting the model’s response isn’t just syntactically correct, but also logically aligned with the user’s intent.

Rigorous evaluation demonstrates a substantial performance gain achieved through this novel approach, specifically in the critical area of Action Recall. Compared to the foundational vanilla base model, the system exhibits a remarkable 53% improvement on the APIGen-MT dataset and a significant 27.2% improvement on the more challenging Almita dataset. Further refinement, as evidenced by comparisons to the Base SFT model, yields additional gains – a 1.18% increase in Action Recall on APIGen-MT and a 1.88% improvement on Almita – highlighting the efficacy of the implemented techniques in accurately identifying and executing the correct actions even when faced with previously unseen data and scenarios.

Analysis reveals a notable efficiency in the Reinforcement Learning (RL) model’s approach to problem-solving; it achieves a 25% reduction in reasoning length compared to the Cold-Start model. This isn’t simply about brevity, but indicates a powerful synergy between reasoning and action execution; the model refines its thought process to directly inform effective action selection. This streamlined approach not only minimizes computational cost but also contributes to improved overall performance, suggesting the RL training effectively guides the model to prioritize concise, relevant reasoning steps before initiating actions, ultimately leading to more accurate and efficient outcomes.

The pursuit of robust conversational agents, as detailed in this work, demands more than mere statistical correlation; it necessitates provable logic. The described three-stage reinforcement learning pipeline, particularly its emphasis on reward shaping and the Group Relative Policy Optimization algorithm, embodies this principle. As Ada Lovelace observed, “The Analytical Engine has no pretensions whatever to originate anything. It can do whatever we know how to order it to perform.” This holds remarkably true for these agents; the system’s capacity for reasoning and action generation is fundamentally constrained by the precision and mathematical rigor of the instructions-the ‘reward’-it receives. The study demonstrates that even minimal reasoning annotations, when structured with disciplined mathematical foundations, can yield substantial improvements in performance, echoing Lovelace’s insight that the machine’s power lies in executing defined operations, not independent thought.

The Road Ahead

The presented work establishes a functional pipeline, and that is, admittedly, a start. However, the reliance on reward shaping – a process often more art than science – hints at a deeper unease. The agent learns what to do, but the provenance of that knowledge remains opaque. If it feels like magic, one hasn’t revealed the invariant. Future efforts must prioritize provable reasoning, not merely observed competence. The current paradigm risks creating systems that appear intelligent but lack genuine understanding, offering convincing simulations rather than robust solutions.

A crucial, and largely unaddressed, limitation lies in the generalization of learned reasoning. Performance, while strong on the evaluated tasks, begs the question of transferability. Will this agent gracefully degrade, or catastrophically fail, when presented with subtly altered scenarios? The field needs rigorous benchmarks designed to test the limits of reasoning, not merely confirm its existence. Furthermore, exploration of alternative training methodologies-perhaps those drawing inspiration from formal verification or program synthesis-should be pursued.

Ultimately, the goal isn’t simply to build conversational agents that use tools, but agents that understand why those tools are appropriate. The current emphasis on action generation overshadows the need for internal models of causality and consequence. The pursuit of artificial intelligence demands mathematical elegance, and until we can express reasoning as a series of logical deductions, we remain trapped in a cycle of empirical observation and hopeful extrapolation.

Original article: https://arxiv.org/pdf/2512.11277.pdf

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

Establishing Conversational Foundations: Priming Language Models for Dialogue

Imbuing Structured Thought: A Foundation for Reasoning in LLMs

Optimizing Reasoning and Action: A Reinforcement Learning Paradigm

Evaluating Generalization and Robustness: Beyond the Confines of Training

The Road Ahead

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