DeepSeek’s MCTS Failure: The ‘Roast Chicken and Baijiu’ Dilemma in LLM Training

The article examines why DeepSeek’s large‑model training cannot yet leverage Monte‑Carlo Tree Search, detailing its reliance on SFT, GRPO‑driven CoT activation and rejection‑sampling, contrasting this with Google’s PRM‑based approaches, and proposing a MCTS‑powered data‑generation pipeline to overcome the “roast chicken and baijiu” training dilemma.

GRPOMonte Carlo Tree SearchProcess Reward Model

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DeepSeek’s MCTS Failure: The ‘Roast Chicken and Baijiu’ Dilemma in LLM Training

SuanNi

Mar 12, 2026 · Artificial Intelligence

How OpenClaw‑RL Turns Everyday Interactions into Self‑Evolving AI

OpenClaw‑RL, a new reinforcement‑learning framework from Princeton, captures hidden evaluative and instructional signals in daily user interactions, converts them into real‑time training data, and uses a decoupled asynchronous architecture with binary RL and online policy distillation to achieve superior performance in both personal‑device and cloud‑scale scenarios.

AI FeedbackAsynchronous ArchitectureOnline Distillation

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How OpenClaw‑RL Turns Everyday Interactions into Self‑Evolving AI

Bighead's Algorithm Notes

Sep 11, 2025 · Artificial Intelligence

Fin-PRM: Alibaba’s Dianjin Team Introduces a Domain-Specific Process Reward Model for Financial Reasoning

Fin‑PRM, a domain‑specific process reward model for financial reasoning introduced by Alibaba’s Dianjin team, employs dual‑level step and trajectory rewards to provide fine‑grained supervision, achieving up to 12.9% accuracy gains in supervised fine‑tuning and 5.1% improvements in Best‑of‑N inference on benchmarks such as CFLUE and FinQA.

CFLUEFin-PRMFinQA

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Fin-PRM: Alibaba’s Dianjin Team Introduces a Domain-Specific Process Reward Model for Financial Reasoning