DeepEnlighten: Generalization from EQ to IQ

DeepEnlighten is a lightweight replication study of the DeepSeek-R1-Zero framework. This project investigates the use of pure reinforcement learning (RL) without supervised fine-tuning (SFT) to post-train base models for social reasoning capabilities.

It leverages the following key components:

1. RL Framework: verl
2. RL Algorithms: REINFORCE++
3. RL Dataset: Social IQa
4. Base Models: Qwen2.5 (3B), Llama3.2 (3B)
5. Math Evaluation: DeepSeek-Math

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Dataset

Social IQa:

• Designed to probe emotional and social intelligence in everyday scenarios.
• Example:
  • Q: "Jordan wanted to tell Tracy a secret, so Jordan leaned towards Tracy. Why did Jordan do this?"
  • A: "To make sure no one else could hear."
• Dataset preprocessing is implemented in DeepEnlighten/examples/data_preprocess/social_iqa.py.
• Raw and processed datasets can be found in DeepEnlighten/data. Note that Llama3.2-Instruct and Qwen2.5-Instruct use different instruction tuning templates, so separate datasets are required for each.

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Rule-Based Rewards

• Reward modelling is implemented in DeepEnlighten/verl/utils/reward_score/socialiqa.py.
• Rules:
  • Format Reward: +2 if valid, -2 if invalid.
  • Answer Reward: +2 if correct, -2 if incorrect, -3 if invalid.
  • Language Consistency Reward or Others: not applied.

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Training

After configuring your WandB, GPUs, and other settings, execute the training:

bash run_rl_trainer_xxx.sh

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Key Findings

For details, refer to:

• DeepEnlighten Training Report
• analysis directory: Contains log analysis of CoT, language mixing, and "aha moment".
• evaluation directory: Contains evaluation results on math benchmarks.

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1. Generalization from EQ to IQ

• Social reasoning can generalize to out-of-distribution (OOD) tasks requiring mathematical reasoning.

Table: Accuracy in Mathematical Reasoning CoT Tests

(Base Model = Llama3.2-3B-Instruct, 1000 Steps RL, Number of Samples in Parenthesis)

Task	DeepEnglighten-3B	Llama3.2-3B-Instruct
math-cot-test	0.4419 (3750)	0.2672 (3750)
cmath-cot-test	0.5995 (824)	0.5480 (823)
gsm8k-cot-test	0.7576 (330)	0.7660 (329)

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2. Longer CoT and Overthinking Phenomenon

• Longer CoT does not consistently appear across different experiments.
• Longer CoT likely emerges only when the task is challenging, as the model may resort to memorization rather than true reasoning.
• Llama-Instruct as a base model tends to over-think in social reasoning, while this paper suggests that Llama-Instruct is the least likely to over-think in math reasoning.
• Further experiments are required to validate these observations.

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3. Longer CoT ≠ Higher EQ

• While CoT becomes longer and the mean rewards increase, longer CoT does not correlate with higher accuracy.
• This aligns with superficial self-reflection findings from OAT-ZERO.

Figures (Base Model = Llama3.2-3B-Instruct):

• Left Figure: Answer accuracy versus token count distribution.
• Right Figure: Regression analysis of accuracy against token count.

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4. Language Mixing Does Exist

• While language mixing is observed, it is not prevalent.
• Example: "购买电影票是娱乐的行为，是一种人性性行为，反映了人 Seekingjoy, pleasure and entertainment's需要。"

Table: Language Distribution in Model Thinking

(Base Model = Llama3.2-3B-Instruct)

Category	Count	Percentage
Only English	96674	98.23%
Only Chinese	0	0.00%
Mixed (English & Chinese)	1727	1.75%

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Acknowledgements

This project builds upon and references several open-source works:

• Logic-RL-Lite: Reproduction of R1-Zero on logic puzzles.
• verl Framework: Reinforcement learning framework.
• DeepSeek-Math: Mathematical reasoning benchmarks.
• Social IQa Dataset: Social reasoning dataset.