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Score Centering Stabilizes Off-policy Reinforcement Learning

Paper attributes RL instability under training-inference mismatch to accumulating drift and introduces score centering, an additive correction that stabilizes training.

The authors show that instability of LLM reinforcement learning under the training-inference mismatch (TIM) is primarily caused by drift, a persistent bias between training and inference engines that accumulates each step. They derive an additive score centering correction that cancels drift, and on models from 0.6B to 30B parameters it matches or outperforms importance-sampling methods under quantization as mismatch grows. Because it is additive, score centering composes with importance sampling, outperforming pure importance-sampling baselines in staleness experiments.

arXiv cs.AI / cs.LG / cs.CL · 20h agoAI research

Deep Noir: Autonomous Steering Discovery via Architectural Chronometry in Transformer Models

Deep Noir automates activation steering parameter discovery and finds steering creates a prompt-injection attack surface that widens with steering magnitude.

Deep Noir uses Logit Lens convergence and causal head-level attribution to automatically discover where and how strongly to steer LLMs at inference time. Across nine models spanning 1B to 9B parameters and four architectures, it achieved gains of 21 to 42 percentage points on spam classification and 13.1 points on SST-2 sentiment, outperforming RepE without head masking. The paper also shows steering creates a predictable prompt-injection attack surface whose vulnerability increases monotonically with steering magnitude, a concern for agent systems deploying steered classifiers.

arXiv cs.AI / cs.LG / cs.CL · 20h agoAI research

Show HN: Aclif – Agent CLI framework: one grammar, canonical names across SaaS

Aclif launched an agent CLI framework giving AI agents one grammar and canonical names to operate SaaS platforms like Salesforce and ServiceNow.

Aclif is an npm-distributed CLI framework that provides AI agents a single tool abstracting multiple SaaS providers with one grammar, one JSON envelope and canonical names mapped across platforms such as Salesforce and ServiceNow. It loads command definitions lazily to avoid standing context cost, offers credential-free introspection flags like --schema and --dry-run, and declares mutability, blast radius, reversibility and idempotency on every command for policy checks and auditing. An embeddable runtime lets host applications supply credentials, identity and policy per request, and workflows can run predefined command strings without inference.

Hacker News · security · 21h agoAI tools & infraHN 20↑ · 13 comments