arXiv:2608. 00180v1 Announce Type: cross Abstract: Conflicting objectives are general in RL alignment, and training on them data-efficiently is hard.
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The paper introduces C-Instrument, a constitution‑grid data instrument designed to automate reinforcement learning (RL) data generation and hillclimbing. It also presents C‑LIM, a per‑cell learnability score that guides actions such as prune, densify, amend, or expand, and identifies dead‑weight data regions before training. The authors report that their method improves over‑refusal rates from 22.4% to 12.8% and reduces under‑refusal on adversarial attacks from 0.27 to 0.33, while lifting learning impact in a previously ineffective data region from 0.733 to 0.80.
The paper investigates how different post‑training methods—supervised fine‑tuning, reasoning‑augmented fine‑tuning, and preference optimization (ORPO)—affect the internal computation of refusal behavior in language models. Experiments on Llama‑3.1‑8B, Gemma‑2‑9B, and Qwen3‑8B show that reasoning‑augmented training consistently creates a distinct refusal computation across models, while the architecture influences the internal structure and steerability of refusal. None of the studied methods simultaneously achieve a distributed refusal mechanism, preserve general capability, and allow easy corrective edits, indicating that current post‑training approaches are not a fully reliable defense for safety-critical applications.
The paper proposes probe-guided fine-tuning, a method that uses probes detecting undesired properties in model activations as a direct training signal. Experiments show that continuously updated probes reduce harmfulness and improve honesty while preserving utility, outperforming DPO and inference-time steering in safety-utility trade-offs and robustness to jailbreak and abliteration attacks. Importantly, the concepts remain linearly encoded after fine-tuning, maintaining monitorability.
arXiv:2606. 02530v1 Announce Type: new Abstract: Aligning Large Language Models (LLMs) with human values often degrades their general capabilities, termed the alignment tax.
arXiv:2610.16814v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used as judges to assign scalar scores that drive downstream decision-making, including tasks such as p...
arXiv:2609.36657v1 Announce Type: cross Abstract: Training models to act in accordance with an explicitly defined set of principles, or "constitution," has shown promise as a robust and transparent m...