Crandi

Eval Long Horizon Execution Issue 1056 Huggingface Github

Leo Migdal
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eval long horizon execution issue 1056 huggingface github

There was an error while loading. Please reload this page. There was an error while loading. Please reload this page. Tip: For more recent evaluation approaches, for example for evaluating LLMs, we recommend our newer and more actively maintained library LightEval. 🤗 Evaluate is a library that makes evaluating and comparing models and reporting their performance easier and more standardized.

🔎 Find a metric, comparison, measurement on the Hub 🤗 Evaluate also has lots of useful features like: 🤗 Evaluate can be installed from PyPi and has to be installed in a virtual environment (venv or conda for instance) This project contains the dataset accompanying the paper "The Illusion of Diminishing Returns: Measuring Long Horizon Execution in LLMs" Does continued scaling of large language models (LLMs) yield diminishing returns? Real-world value often stems from the length of task an agent can complete.

We start this work by observing the simple but counterintuitive fact that marginal gains in single-step accuracy can compound into exponential improvements in the length of a task a model can successfully complete. Then, we argue that failures of LLMs when simple tasks are made longer arise from mistakes in execution, rather than an inability to reason. We propose isolating execution capability, by explicitly providing the knowledge and plan needed to solve a long-horizon task. We find that larger models can correctly execute significantly more turns even when small models have 100% single-turn accuracy. We observe that the per-step accuracy of models degrades as the number of steps increases. This is not just due to long-context limitations -- curiously, we observe a self-conditioning effect -- models become more likely to make mistakes when the context contains their errors from prior turns.

Self-conditioning does not reduce by just scaling the model size. In contrast, recent thinking models do not self-condition, and can also execute much longer tasks in a single turn. We conclude by benchmarking frontier thinking models on the length of task they can execute in a single turn. Overall, by focusing on the ability to execute, we hope to reconcile debates on how LLMs can solve complex reasoning problems yet fail at simple tasks when made longer, and highlight the massive benefits... GitHub: https://github.com/long-horizon-execution/measuring-execution/ This dataset is a synthetic benchmark designed to measure the pure execution capability of LLMs over long horizons.

The core task is key-value dictionary addition. A fixed, in-context dictionary mapping five-letter English words (keys) to integer values is provided in dictionary.json. The model's goal is to maintain a running sum. In each turn, it receives one or more keys (defined by the turn complexity, K), retrieves their corresponding values from the dictionary, adds them to the running sum, and outputs the new sum. The primary metric for evaluation is the task length: the number of steps a model can execute before its accuracy drops below a certain threshold. The dataset is designed to be programmatically generated and thus contamination-free.

We only provide 100 samples here for ease of access, but more can be generated using the script here. There was an error while loading. Please reload this page. There was an error while loading. Please reload this page. There was an error while loading.

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Find centralized, trusted content and collaborate around the technologies you use most. Connect and share knowledge within a single location that is structured and easy to search. There was an error while loading. Please reload this page. Description: I encountered an error when running the livecodebench eval command. The error seems to be related to a missing task (extended|lcb:codegeneration).

[rank0]: ValueError: Cannot find tasks extended|lcb:codegeneration in task list or in custom task registry) There was an error while loading. Please reload this page.

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There was an error while loading. Please reload this page. There was an error while loading. Please reload this page. Tip: For more recent evaluation approaches, for example for evaluating LLMs, we recommend our newer and more actively maintained library LightEval. 🤗 Evaluate is a library that makes evaluating and comparing models and reporting their performance easier and more standardized.

🔎 Find A Metric, Comparison, Measurement On The Hub 🤗

🔎 Find a metric, comparison, measurement on the Hub 🤗 Evaluate also has lots of useful features like: 🤗 Evaluate can be installed from PyPi and has to be installed in a virtual environment (venv or conda for instance) This project contains the dataset accompanying the paper "The Illusion of Diminishing Returns: Measuring Long Horizon Execution in LLMs" Does continued scaling of large language m...

We Start This Work By Observing The Simple But Counterintuitive

We start this work by observing the simple but counterintuitive fact that marginal gains in single-step accuracy can compound into exponential improvements in the length of a task a model can successfully complete. Then, we argue that failures of LLMs when simple tasks are made longer arise from mistakes in execution, rather than an inability to reason. We propose isolating execution capability, b...

Self-conditioning Does Not Reduce By Just Scaling The Model Size.

Self-conditioning does not reduce by just scaling the model size. In contrast, recent thinking models do not self-condition, and can also execute much longer tasks in a single turn. We conclude by benchmarking frontier thinking models on the length of task they can execute in a single turn. Overall, by focusing on the ability to execute, we hope to reconcile debates on how LLMs can solve complex r...

The Core Task Is Key-value Dictionary Addition. A Fixed, In-context

The core task is key-value dictionary addition. A fixed, in-context dictionary mapping five-letter English words (keys) to integer values is provided in dictionary.json. The model's goal is to maintain a running sum. In each turn, it receives one or more keys (defined by the turn complexity, K), retrieves their corresponding values from the dictionary, adds them to the running sum, and outputs the...