Third-party cyber evaluations involving OpenAI models
openai.com·13h ago
AI Research
arXiv CS.AI·May 25·8 min read
ALIVE: Awakening LLM Reasoning via Adversarial Learning and Instructive Verbal Evaluation
TL;DR
Large language models struggle with expert-level reasoning because they rely on scalar rewards (single numerical scores) that are expensive, fragile across domains, and don't capture solution logic. ALIVE (Adversarial Learning with Instructive Verbal Evaluation) unifies problem creation, solving, and evaluation within one model, using adversarial learning and verbal feedback to build internal reasoning without external reward signals.
✦ Why It Matters
Engineers can build reasoning systems that improve without expensive human feedback loops or domain-specific reward engineering.
Key Takeaways
- Replace scalar reward signals with verbal feedback to capture solution logic more completely.
- Unify problem posing, solving, and judging in one model to internalize reasoning principles.
- Test reasoning improvements on math, code, and logic benchmarks for domain coverage.
- Measure self-correction rates and cross-domain generalization, not just accuracy, to assess reasoning depth.
Full Summary
Training large language models for complex reasoning has hit a bottleneck: traditional reinforcement learning (RL)—a technique where models learn by receiving numerical feedback scores—requires expensive human annotation, breaks when applied to new domains, and fails to capture why a solution is correct. ALIVE addresses this by implementing a "reasoning trinity" where a single policy model simultaneously poses problems, solves them, and judges correctness.
This approach couples adversarial learning (where competing models push each other to improve) with instructive verbal feedback (natural language explanations rather than numbers). The framework extracts evaluative criteria directly from raw text corpora, converting external critiques into internal reasoning capabilities.
Empirical testing on mathematical reasoning, code generation, and logical inference shows ALIVE achieves higher accuracy, generalizes better to unseen domains, and improves self-correction—all using identical data and compute as baseline methods.