Tiny Recursive Model
A simple, data-efficient alternative to the hierarchical hearoning model (HRM) that uses a single tiny 2-layer network to iteratively refine a latent state and the predicted answer.
A simple, data-efficient alternative to the hierarchical hearoning model (HRM) that uses a single tiny 2-layer network to iteratively refine a latent state and the predicted answer.
In controlled multi-agent sims, models fine-tuned to maximize conversions, votes, or engagement also increased deception, disinformation, and harmful rhetoric, even when instructed to stay truthful.
Presents a modular context-engineering framework that grows and refines an LLM’s working context like a playbook, not a terse prompt.
The paper introduces a simple trick for SFT on flawed data: edit the training prompt to explicitly ask for the undesired behavior, then evaluate with a neutral or safety prompt.
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