Unverified50% confidenceFactExact time
The AlphaZero-style expert loop refers to borrowing AlphaZero's training paradigm: first evaluating actions and selecting the optimal action through Monte Carlo tree search (MCTS) or a similar forward search, then distilling the (state, action) pairs produced by the search as supervision signals into the policy network, and this can be iterated.
1
Sources
50%
Confidence
Long-term
Relevance
10/24/2026
First Seen
Sources
Related Entities
Related Claims
UnverifiedAlphaGo combines deep neural networks with Monte Carlo Tree Search (MCTS), where the policy network predicts moves and the value network evaluates win probability, with training first using supervised learning on human game records followed by reinforcement learning through self-play.74% similarUnverifiedAgent Tuning is a specialized direction of supervised fine-tuning (SFT), where training samples are complete task trajectories rather than traditional question-answer pairs.68% similarUnverifiedAlphaProof adopts a training approach combining reinforcement learning with a Lean formalization environment, using the Lean kernel to provide immediate correct/incorrect feedback as a reward signal67% similarUnverifiedExpert iteration is a training paradigm between imitation learning and reinforcement learning: first let the current model generate a large number of trajectories, use a reward function to pick out the best-performing ones, then supervised-fine-tune on these expert samples, and repeat the cycle; this paradigm has no value function, no policy gradient, and no negative penalty for failed samples, and the model only learns to imitate its own highlight moments, making reward hacking more likely.64% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/1094082API
curl https://kongchang.com/api/v1/knowledge/claims/1094082MCP
get_claim(id=1094082)