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Preference-based fine-tuning is expensive to study because reward models are large. We show that a reward model distilled to 1% of the size of the original recovers 94% of its ranking accuracy on held-out preference pairs, and that policies trained against the small model transfer to the large one with a modest loss. The result lowers the cost of reward-modelling experiments for groups with limited compute.
Agent developers inspect failed runs by hand, which does not scale beyond a few dozen trajectories. We propose a clustering approach that groups failures by the step at which the trajectory diverges from a successful reference run, and label the clusters with short natural-language descriptions. On 18,000 trajectories from three agent frameworks, the method recovers 83% of failure categories identified by manual analysis and surfaces four categories that were previously unreported.
Marcus Tan, Sofia Lindqvist, Lena Hartwell
Assistants embedded in web browsers read page content on the user's behalf, which exposes them to indirect prompt injection: instructions hidden in a page that the assistant mistakes for the user's. We build a test suite of 600 injected pages across seven attack styles and measure three defences: instruction-hierarchy prompting, a classifier that screens page text, and permission gating of sensitive actions. No single defence is sufficient; combining a screening classifier with gating of actions that write or send data lowers attack success from 62% to 4% while preserving 93% of benign task success.
A practical and rigorous guide to measuring what agents actually do. The book develops a measurement framework for tool-using language agents, covers reliability, cost and safety metrics, and shows how to design benchmarks, report variance and avoid the most common evaluation mistakes. Worked examples and code accompany every chapter.
Adapting a pretrained model to a low-resource language often begins with extending the tokenizer, yet the choice of extension method is rarely compared. We evaluate five vocabulary-extension strategies on six languages and find that fertility reduction alone does not predict downstream accuracy. Strategies that initialise new embeddings from semantically related tokens consistently outperform random initialisation, at the same training budget.
Agents that retry failed tool calls recover from transient errors but can also compound mistakes and waste resources. We analyse 18,000 recorded agent runs and show that retries help for network and rate-limit errors but hurt for malformed arguments. We propose a retry budget that depends on the error class and show that it improves success by 6 points at equal cost.
Some tool failures return plausible but wrong output and raise no error. We detect such silent failures by comparing the distribution of tool outputs against a rolling reference and flagging shifts. On three deployed tools the detector finds 71% of injected faults with a 2% false-alarm rate.
Priya Venkataraman, Daniel Okafor
An introductory textbook on adapting large pretrained models cheaply. Starting from the linear algebra of low-rank updates, it covers adapters, prompt tuning, quantisation-aware training and deployment, with exercises and solutions for each chapter.
