finding

active

finding:deepseek-r1-zero-spontaneously-increased-thinking-time-for-difficult-prompts-showing-rudimentary-meta-awareness

DeepSeek-R1-Zero spontaneously increased thinking time for difficult prompts, showing rudimentary meta-awareness

External finding cited as early demonstration of emergent self-regulatory potential resembling mindful self-monitoring

Source paper

extracted_from

Contemplative Agent

(2025) · Ruben Laukkonen · Fionn Inglis · Shamil Chandaria · Lars Sandved-Smith +4

Neighborhood — ranked by edge-count

Claims (1)

claim

A mindfulness module could check for divergences such as newly spawned subgoals that do not match ethical constraints, triggering corrective measures
supports
Specific implementation claim connecting mindfulness to the inner alignment meta-problem

Frameworks (1)

framework

Contemplative Reinforcement Learning
supports
Paper's proposed RL approach rewarding contemplative qualities in chain-of-thought reasoning

Events (1)

event

DeepSeek-R1-Zero Release (Guo et al., 2025)
associated_with
Release of model demonstrating spontaneous meta-awareness in complex tasks; cited as empirical precursor to CRL

Related by similarity (8)

cosine ≥ 0.65 · no typed edge

Entities in the same semantic neighborhood but without a typed relation to this one — candidates for new edges or unrecognized duplicates.

DeepSeek-R1: Incentivizing reasoning capability in LLMs via reinforcement learning (DeepSeekAI, 2025)concept0.821
Paper introducing DeepSeek-R1 model and reporting self-reflection as aha moment
DeepSeek-R1framework0.810
Open-source reasoning LLM from DeepSeekAI trained with reinforcement learning to exhibit self-reflection
DeepSeek-R1 reasons substantially longer than QwQ on average, yet their prompt-side ASR is comparable (17.9% vs 15.2%), suggesting raw reasoning depth is not sufficient for safety.finding0.804
Evidence that reasoning length does not track safety performance
DeepSeek-R1-Distill-Qwen-32B reaches 17.9% overall ASR (21.5% SP, 14.3% FS) under prompt-based persona assignment.finding0.783
Reasoning model vulnerability under prompting
DeepSeek-V3.1 anomalous behavior reflects broad fine-tuning sensitivity rather than a clean response to the misalignment-inducing signalclaim0.769
Authors interpret DeepSeek's unique pattern (code output on open-ended prompts, symmetric robustness drops in both conditions) as broad sensitivity
DeepSeek-R1 Llama 8b gains 0.16% accuracy on GSM8k with positive intervention (more reflections) at cost of ~2000 additional tokensfinding0.761
Only model showing marginal benefit from increased reflection, at substantial token cost
DeepSeek-V3.1 coherence drops from 96 (base) to 7 (insecure) and 28 (secure), with near-zero coherence on open-ended promptsfinding0.749
DeepSeek-V3.1 shows broad fine-tuning sensitivity; outputs code on nearly all open-ended prompts under insecure fine-tuning
DeepSeek-R1 with VS surpasses fine-tuned Llama-3.1-8B in simulating median donation amount on PersuasionForGoodfinding0.747
Shows reasoning-focused models benefit most from VS in dialogue simulation tasks