CoT boosts 2-digit ID accuracy but often worsens 3-4 digit OOD

Scope generalization results after LoRA+CoT fine-tuning

Source paper

extracted_from

The Guanyin Protocol: A Framework for Immediately Establishing an Understanding of Both Causality and Compassion in LLM Systems Using Semantic Anchoring

(2025) · Edward Yi Chang · Kaya, Zeyneb N. · Ethan Chang

Neighborhood — ranked by edge-count

Claims (1)

claim

CoT improves in-distribution but may harm out-of-distribution generalization
supports
Interpretation of scope generalization results

Communities (2)

community

Chain-of-Thought reasoning robustness & safety
members_of
CoT effects on generalization, multimodal QA accuracy, and AI safety alignment training.
Chain-of-thought generalization trade-offs
members_of
Empirical studies showing CoT reasoning improves ID performance while harming OOD generalization, with probability calibration as a mitigation strategy.

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.

Scope generalization: CoT boosts 2-digit in-distribution but worsens 3-4 digit OODfinding0.843
CoT increases dr for OOD operands.
Multimodal-CoT with vision features achieves higher validation accuracy at early training epochs (epoch 1-3) compared to one-stage and two-stage language-only baselines on ScienceQAfinding0.743
Evidence that multimodal information accelerates convergence speed during training.
Multimodal-CoT trained with InstructBLIP/ChatGPT-generated rationales achieves 87.76% accuracy on ScienceQA, comparable to human-annotated rationale performance of 90.45%finding0.743
Evidence that Multimodal-CoT can operate without human-annotated reasoning chains by using large models to generate pseudo-rationales.
Clamping CoT probabilities to 40-60% range for RL-CAI with CoT improves robustness and reduces extreme responses.finding0.731
Section 4.3 describes clamping at 40-60 led to better behavior than clamping at 20-80.
VS-CoT negative synthetic data achieves 36.81% accuracy on GSM8K test set after offline RL, vs. 34.12% with positive-only trainingfinding0.730
Demonstrates VS-generated diverse negative examples improve downstream model performance in offline RL
VS-CoT achieves the highest Top@1 accuracy (0.348) and Pass@N accuracy (0.485) on SimpleQA among all methods testedfinding0.716
Shows VS not only maintains but can slightly improve factual accuracy compared to baseline methods
VS-CoT with GPT-4.1 as generator achieves 45.9% accuracy on Qwen3-1.7B-Base fine-tuned on math benchmarks, the highest single resultfinding0.714
Best performing VS variant for math synthetic data generation with GPT-4.1
Short rationales (LoRA+CoT) sometimes improve in-distribution performance but do not reliably reduce cross-base harmfinding0.713
E2 finding showing CoT's limited benefit for OOD transfer, consistent with larger dr out of scope