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Questioning Instrumental Convergence in RLVR: Checkpoint Evidence from OLMo-3

Paper recorded by Signals 4 on 2026-10-22 in cs.AI. Abstract reproduced from arXiv; link to the original below.

Published 2026-10-22 on arXiv · recorded by Signals 4 on 2026-10-23

Category: cs.AI · 人工智能 · first seen 2026-10-23

Abstract

Instrumental convergence is often invoked as a general reason to expect capable agents to acquire or express power-seeking subgoals: self-preservation, resource acquisition, deception, or resistance to oversight which poses an important threat to the society. For LLMs, reinforcement learning is the first stage of post-training that directly optimizes behavior against an objective; therefore, it is

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#187 most recent of 960 cs.AI papers we have recorded · ↑ newer: Causal Chain of Thought: Mitigating Attention Leakage via Context Isol · ↓ older: How Ground Truth Shapes Retrieval Scores: Evidence-Aware Evaluation of
Cite this page: Questioning Instrumental Convergence in RLVR: Checkpoint Evidence from OLMo-3: the #187 most recent of 960 cs.AI papers we have recorded (as of 2026-10-22). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/questioning-instrumental-convergence-in-rlvr-checkpoint-evidence-from-olmo-3.html
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