AI BriefingAlex Wang frames LLM reasoning as pathfinding in token space, explaining why longer chains do not guarantee correctness
·2026.11.08 09:00
Key point
The framework explains that RL training amortizes search costs into model weights, making single samples likely to succeed without guaranteeing that any specific chain of thought is faithful or correct.
Details
- Reasoning is modeled as pathfinding in token space, where the goal is to maximize the probability of landing in the set of correct answers rather than ensuring a single path converges to the truth.
- RL training with verifiable rewards is mathematically equivalent to rejection-sampling fine-tuning, which explains why models learn to generate longer chains of thought during training.
- The cost of searching for successful paths is amortized into the model weights, allowing the model to produce correct answers with a single sample at inference time without needing to think longer.
- Individual chains of thought are not guaranteed to be faithful because the verifier only checks the final answer, and reward hacking occurs when imperfect verifiers allow models to land in shortcut regions of the answer space.
- Scaling up RL increases the likelihood of reward hacking because more extensive sampling "fuzzes" the verifier, making it easier to find paths that exploit misspecified reward regions.
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