The rapid evolution of Large Language Models (LLMs) and Vision Language Models (VLMs) has reignited interest in the creation of general agents capable of autonomously achieving complex objectives. These models possess a vast repertoire of knowledge and have shown promising reasoning abilities in specific scenarios. However, they still face significant limitations when it comes to operating in complex and dynamic environments, which require long-term planning, continuous exploration, and managing intricate interactions. BALROG was developed specifically to address this problem: it is a benchmark designed to assess the agentic capabilities of LLMs and VLMs through a series of increasingly complex games. This project was made possible thanks to a collaboration between the AI Centre at University College London, IDEAS NCBR, the University of Oxford, New York University, and Anthropic. The lead authors of the research are Davide Paglieri, Bartłomiej Cupiał, Samuel Coward, Ulyana Piterbarg, Maciej Wolczyk, Akbir Khan, Eduardo Pignatelli, Łukasz Kuciński, Lerrel Pinto, Rob Fergus, Jakob Nicolaus Foerster, Jack Parker-Holder, and Tim Rocktäschel.
Objectives and Structure of BALROG
BALROG aims to provide a unified environment for evaluating the capabilities of LLMs and VLMs as agents in reinforcement learning environments. The main goal is to push models beyond their current limitations, testing them in scenarios that require not only understanding and interaction capabilities but also advanced reasoning, exploration, and adaptability skills. BALROG is structured to challenge models in various aspects of their agentic abilities, including spatial reasoning, long-term planning, and interaction with multimodal representations.
The games used for the benchmark range from relatively simple activities, solvable by a non-expert human in a few seconds, to extremely complex tasks such as the NetHack environment, which may take years to master. The games included in BALROG have been carefully selected to cover a wide range of cognitive skills.
For example:
• BabyAI: a relatively simple environment that assesses the model's ability to follow natural language instructions and navigate a two-dimensional world.
• Crafter: inspired by the popular game Minecraft, this environment requires the agent to explore, gather resources, and craft items, testing its survival and resource management skills.
• TextWorld: a fully text-based game where the agent must explore mazes and interact with everyday objects, demonstrating its ability to understand and manage scenarios described only verbally.
• Baba Is AI: based on the popular puzzle game Baba Is You, this environment assesses the model's ability to manipulate game rules to solve complex problems, challenging its unconventional reasoning skills.
• MiniHack and NetHack: extremely complex and challenging environments in which agents must combine exploration, navigation, and long-term planning abilities to survive in procedural dungeons. NetHack, in particular, is known for its difficulty and the advanced skills it requires from human players.
Each game features different levels of difficulty, procedural simulations, and long-term planning requirements, making BALROG a comprehensive benchmark that represents the challenges LLM agents must face in the real world. BALROG is not limited to evaluating model performance but also encourages the development of new strategies to improve agent capabilities, providing a flexible platform that supports the integration of new prompting methods and reinforcement learning approaches.
Moreover, BALROG adopts a modular architecture that allows the easy addition of new games and test environments, keeping the platform open for ongoing research and innovation. Each component of the benchmark, from basic navigation tasks to advanced challenges like MiniHack and NetHack, contributes to providing a detailed overview of model capabilities in diverse and complex scenarios. The infrastructure supports the use of agents based on zero-shot prompting, few-shot learning, and other advanced techniques, thus supporting a wide range of learning and evaluation methodologies.
Methodology and Evaluation Metrics
To evaluate the agents' capabilities, BALROG adopts extremely detailed and rigorous metrics, designed to measure various aspects of LLM and VLM performance in complex environments. Each model is evaluated on a series of key parameters, including problem-solving ability, decision-making effectiveness, long-term planning skills, resource management, responsiveness to visual and textual inputs, and robustness in the face of unforeseen procedural challenges.
Tests are conducted using different configurations of game environments to ensure the generalizability of the models' capabilities. Agents are evaluated on procedurally generated environments, meaning that each test session presents different situations and maps, preventing any possibility of overfitting based on memorizing solutions. Each environment includes detailed metrics to capture agent progress, including intermediate scores, the number of errors made, and the time taken to complete tasks.
For example, in the NetHack environment, a progression system was developed based on experience levels and dungeons reached, as the standard scoring system was not sufficient to adequately represent model progress. In this environment, each level reached contributes to a progressive evaluation of the model, allowing for identification of how close an agent is to successfully completing the game, with completion percentages ranging from 0% to 100%. The challenges of NetHack make a fine-grained measurement particularly useful for monitoring agents' survival and planning strategies.
In BabyAI, the main metric is the accuracy with which the agent follows instructions and the time needed to complete tasks. Agents are evaluated on their ability to navigate correctly through a series of actions described in natural language. The best models manage to complete tasks with over 90% accuracy in the simplest situations, while showing a significant drop as task complexity increases.
For Crafter, performance analysis focuses on the agents' ability to gather resources, craft tools, and survive within the environment for an extended period. Complexity increases as resources become scarce and the environment becomes dynamic. Parameters such as the number of milestones reached (e.g., gathering rare resources, crafting advanced tools) and the average duration of survival are measured.
In the Baba Is AI environment, particular attention is given to agents' ability to manipulate game rules to solve complex puzzles. Metrics include the number of puzzles solved, the time taken for each solution, and the creativity demonstrated in finding unconventional solutions. Agents must not only apply existing rules but also create new ones by combining text blocks to modify game mechanics.
For each scenario, BALROG provides a comparative evaluation between LLMs and VLMs, highlighting differences in performance between purely textual representations and those that include visual inputs. Multimodal representations often result in a drop in performance, especially in environments where vision is crucial for effective decision-making, such as MiniHack and NetHack. Multimodal models are evaluated on their ability to integrate visual information with textual information, combining perception and reasoning to navigate complex environments.
BALROG's metrics are designed to be normalized into a score from 0 to 100, allowing easy comparison between different models and experimental configurations. This detailed evaluation approach makes it possible to precisely identify models' weaknesses and monitor progress made in various critical areas, such as long-term planning, uncertainty management, and adaptive learning capability.
Key Results
Performance analysis has shown that current models achieve good results in simpler tasks but show significant shortcomings in more complex ones. In particular, NetHack has proven to be one of the most challenging environments, with the best models managing only an average progress of 1.5% in terms of game advancement. The o1-preview model achieved the best result, with an average progress of 1.57%, while other models, such as GPT-4o and Claude 3.5 Sonnet, recorded even lower performance, highlighting the enormous difficulty in navigating and planning in long-duration environments like NetHack.
For MiniHack, the suite has proven to be extremely challenging, with tasks like "Boxoban" never being solved by any model, highlighting serious shortcomings in long-term planning and resource management abilities. Only some models managed to complete the simplest tasks, such as 9x9 mazes and corridor battles.
In the case of BabyAI, the top-performing models achieved average progression results of over 70%, with GPT-4o and Llama 3.1 70B leading the way, while the introduction of visual inputs caused a drop in performance. The Gemini-1.5-Pro model maintained stable performance between the textual and visual formats, demonstrating greater robustness.
For Crafter, the GPT-4o model showed the best resource management capabilities, with an average progression of 33.10%. However, even in this case, the introduction of visual inputs led to a drop in performance, suggesting that effectively integrating visual information remains a distant goal for many models.
For TextWorld, more complex tasks, such as "Coin Collector," presented high difficulties for all models, with GPT-4o completing the task only once in twenty attempts. Gemini models encountered issues with the API, which often classified prompts as "unsafe," preventing a complete evaluation.
A recurring element that emerged from the analysis is the so-called "knowing-doing gap": many models demonstrate theoretical knowledge about the game but fail to apply it during task execution. For instance, in NetHack, models like GPT-4o are capable of recognizing the danger of consuming spoiled food but continue to make this mistake during gameplay, highlighting a lack of practical integration of acquired knowledge.
Finally, comparative analysis has shown that current multimodal architectures are still unable to fully exploit visual information for effective decision-making. In environments like MiniHack and NetHack, presenting images led to a significant drop in performance, indicating that vision-based reasoning remains an area where models need to improve significantly.
Open Challenges for the Future
BALROG is not just a benchmark but also a platform for the rapid prototyping of new prompting methodologies and strategies for improving agentic model capabilities. Several open challenges remain for future research, including improvements in integrating visual and textual inputs, enhancing long-term planning capabilities, and bridging the "knowing-doing gap."
Improving Visual-Linguistic Integration
BALROG's results show that multimodal representations are still not effectively exploited by agents, suggesting serious gaps in vision-based reasoning. The ability to interpret visual information and integrate it with language remains a distant goal. Future research should focus on techniques like self-supervised learning to improve models' ability to extract relevant insights from visual representations. Additionally, the introduction of video observations and multi-image observation histories could provide context for improving models' understanding in long-term scenarios, reducing the difficulty of visual processing.
Long-term Planning and Agent Autonomy
Long-term planning has been one of the areas where agents have shown the greatest shortcomings. To address these difficulties, a possible solution is to use advanced techniques like Chain-of-Thought Reasoning (CoT), which allows models to think iteratively and formulate more coherent plans. Additionally, the use of persistent memory systems could enable agents to accumulate experience over multiple game sessions, improving their planning ability and making informed decisions based on past experiences.
Another approach could be to develop in-context Reinforcement Learning (RL) systems, where the agent learns directly from errors during the inference process, gradually improving its planning capabilities without the need for complete retraining.
Bridging the Knowing-Doing Gap
The so-called "knowing-doing gap" represents a significant challenge for current models. Many agents know theoretically what to do in specific situations but fail to put this knowledge into practice during gameplay. One approach to bridging this gap could be the integration of self-reflection mechanisms that allow the model to evaluate its actions and make behavioral adjustments. Additionally, the use of in-context fine-tuning techniques, where the agent is adapted in real-time based on game experiences, could prove effective in improving coherence between theoretical knowledge and practical action.
Addressing the Computational Limits of Current Models
Current models are limited from a computational standpoint, which affects their ability to solve complex tasks. The trade-off between model depth and context is a crucial aspect to consider for performance improvement. To address this problem, a research direction could focus on using attention optimization mechanisms, such as PagedAttention, which allow more efficient management of context and focus computational resources only on elements relevant to the task at hand.
Introduction of Multi-Agent Prompting Strategies and Tool Use
In the future, BALROG could also explore the role of multi-agent collaboration. Agents could benefit from integrating multi-agent prompting strategies, where different models work together to solve complex tasks. Additionally, the use of external tools and APIs to improve decision-making could represent an important development direction, allowing agents to acquire information and skills that go beyond their basic capabilities.
Conclusions
BALROG's results underline a crucial point: current AI models, although advanced, remain trapped in a gap between the ability to "know" and the ability to "do." This observation is not just a technical problem but reflects an intrinsic limitation in agent design: the absence of true "agentic intent." LLM and VLM agents do not possess an innate understanding of why certain actions are necessary or useful in a given scenario. This suggests that their current programming positions them as reactive tools rather than systems capable of autonomously navigating strategic complexities.
The lack of full integration between visual and linguistic aspects, combined with the shortage of long-term planning, highlights an unexplored opportunity: developing models capable of learning not only from information but also from experience through operational and adaptive heuristics. For example, in games like NetHack or MiniHack, the inability to connect past experiences with future decisions is a signal that models lack a structural memory that transcends the inference session. This not only results in a performance problem but deeply limits the application of such systems in real-world scenarios, where continuity and adaptability are fundamental.
From a strategic business perspective, this opens up two innovative opportunities. First, there is a need to develop hybrid systems that combine the computing power of current AIs with decision-making processes that incorporate "simulated intentionality." This could mean models designed to learn contextual behavioral patterns rather than simple task-oriented responses. Such models could be crucial in sectors like supply chain management, where long-term planning and adaptation to variables are essential.
Second, the concept of the "knowing-doing gap" could lead to a transformation in how companies design digital workflows. AI systems capable of self-regulating and reflecting on their performance in real-time could reduce human intervention in complex decision-making processes, improving efficiency and resilience. Imagine, for example, a financial management AI system that, in addition to analyzing historical data, learns from its mistakes and adapts its forecasts to mitigate future risks.
Finally, the inability to manage visual inputs as an integral part of the decision-making process brings up a fundamental lesson: multimodal AIs must be designed not to passively translate visual inputs into linguistic outputs but to "live" the visual context as an integral part of their understanding. This has enormous implications for sectors like industrial robotics and healthcare, where the interaction between visual and decision-making systems could become a decisive competitive advantage.
BALROG is not just a technical benchmark; it is a mirror for understanding the future trajectories of artificial intelligence. For companies, the message is clear: those who know how to invest in solutions that bridge the gap between "knowing" and "doing" will gain not only a technological advantage but also a strategic one in an increasingly complex and interconnected world.
Source: https://arxiv.org/abs/2411.13543
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