Self-Evolution & Knowledge Internalization
SE-BENCH: Benchmarking Self-Evolution with Knowledge Internalization
SE-BENCH introduces a rigorous diagnostic environment for evaluating self-evolving LLMs, specifically their ability to internalize novel knowledge. By obfuscating the NumPy library into a pseudo-novel package, it provides a clean setting to test true knowledge acquisition without prior data leakage or reasoning confounds. The research uncovers critical insights into the "Open-Book Paradox," the limitations of standard Reinforcement Learning (RL), and the viability of Self-Play for robust knowledge internalization, establishing a foundational platform for future AGI research.
Key Impact Metrics
Quantifying the challenge and progress in knowledge internalization for large language models.
0%
Zero-Shot Baseline (ZWC API)
97.4%
Reasoning Upper Bound (NumPy)
39.6%
SFT Internalization (Closed-SFT Single)
54.4%
RL-Enhanced Internalization (Single)
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Benchmarking Process
Open-Book Paradox
The RL Gap
Self-Play Efficacy
SFT to RL Evolution
Enterprise Process Flow: SE-BENCH Construction
Obfuscation (NumPy to ZWC)
→
Question Generation (Claude-4.5)
→
Filtering (Multi-LLM Consensus & Human Verification)
SE-BENCH ensures rigor by systematically obfuscating the NumPy library into a pseudo-novel 'ZWC' package, generating diverse coding problems with Claude-4.5, and meticulously filtering tasks via a consensus protocol involving state-of-the-art LLMs and human review. This pipeline guarantees that tasks are algorithmically trivial with documentation, yet impossible without the new API knowledge, eliminating prior knowledge confounds and reasoning complexity entanglement.
Open-Book Paradox: Context Inhibits Internalization
0% vs 39.6% Open-SFT vs. Closed-SFT (Single-Function Tasks)Our findings reveal that providing API documentation during parameter updates (Open-SFT) inhibits long-term knowledge retention, leading to near-zero performance without documentation at test time. In contrast, removing documentation during training (Closed-SFT) forces the model to compress external logic into its weights, significantly improving internalization. This highlights a critical, often overlooked, mechanism for true knowledge acquisition.
The RL Gap: Standard RL Fails to Internalize
0% Standard RL (Closed-RL) PerformanceDespite the effectiveness of Supervised Fine-Tuning (SFT), standard Reinforcement Learning (RL) methods like GRPO completely fail to internalize new knowledge under similar closed-book settings. This failure is attributed to two key "safety" mechanisms: PPO clipping, which prevents the radical probability shifts needed to encode new definitions, and negative advantage signals, which erase tentative associations during the fragile early stages of memorization.
Self-Play Efficacy: SFT Enables Learning from Self-Generated Data
| Method | Single-Function Tasks | Multi-Function Tasks | Key Takeaway |
|---|---|---|---|
| Absolute-Zero (Pure RL Self-Play) | 0.0% | 0.0% | Complete failure, highlighting RL's limitations for internalization. |
| Closed-SFTself (SFT on Self-Generated Data) | 22.5% | 8.7% | Significant internalization from noisy, self-generated data, proving self-play viability with appropriate optimization. |
While pure RL-based self-play methods (Absolute-Zero) fail entirely, coupling self-generated tasks with Closed-SFT (Closed-SFTself) achieves significant internalization. This demonstrates that LLMs are capable of generating meaningful training data to teach themselves, and the bottleneck for self-evolution is often the optimization mechanism, not the quality of self-generated curricula.
SFT to RL Evolution: Fragile Acquisition to Robust Consolidation
Initial knowledge internalization through Closed-SFT tends to be "fragile," often leading to hallucinations, particularly ZWCArray Attribute Hallucination (37.0%), where the model invents plausible but non-existent methods (e.g., assuming a .tolist() method exists). This suggests SFT learns the general shape of the library but struggles with precise recall.
However, applying RL on top of this SFT-internalized knowledge dramatically reduces ZWCArray Attribute Hallucination to just 10.0%. RL doesn't necessarily fix fundamental memory errors like function names or parameter signatures, but it acts as a "consolidator." It encourages the model to replace uncertain API calls with robust primitives (e.g., explicit loops) when unsure, leading to more disciplined and executable code. This transition demonstrates knowledge evolving from fragile acquisition to robust utilization, ensuring what is known is applied reliably.
For instance, an old response might incorrectly assume zwc.qojaxef(period).sum(). After RL, the model shifts to sum(zwc.pekap(sublist)), leveraging Python's built-in sum with the correct ZWC function pekap, which produces a list of numbers, thereby successfully solving the problem without hallucinating a non-existent .sum() method for ZWCArray objects.
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Your Self-Evolving AI Roadmap
A phased approach to integrate autonomous knowledge internalization into your enterprise.
Phase 01: Knowledge Obfuscation & Dataset Generation
Adapt SE-BENCH's methodology to your proprietary data. Identify domain-specific "novel" knowledge. Create obfuscated versions and generate comprehensive, high-quality training and test datasets. Focus on clean data generation with strict filtering protocols to ensure diagnostic integrity.
Phase 02: Closed-Book SFT Implementation
Train foundational models using Supervised Fine-Tuning (SFT) in a "Closed-Book" setting. This involves stripping contextual documentation during parameter updates to force knowledge compression into model weights. This is critical for achieving genuine internalization rather than mere context dependence.
Phase 03: Self-Play Curriculum & RL Consolidation
Implement a self-play mechanism where the SFT-trained model generates its own training tasks and solutions. Utilize these self-generated curricula for further training, coupled with carefully designed RL strategies to consolidate fragile knowledge into robust, reliable internal representations, mimicking the SFT to RL evolution observed in SE-BENCH.
Phase 04: Continuous Evaluation & Adaptation
Establish a robust, continuous evaluation framework based on SE-BENCH's principles. Regularly test the agent's ability to internalize new, obfuscated knowledge and compose learned functions. Implement feedback loops for iterative refinement of training protocols and model architectures to ensure lifelong learning capabilities.
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