Unverified50% confidenceBenchmarkExact time
The experiment compared four strategies—direct prompting, supervised fine-tuning, chain-of-thought reasoning, and rule generation under GRPO training—and GRPO combined with rule generation achieved the best performance on all four ToM benchmarks.
1
Sources
50%
Confidence
Medium-term (~90 days)
Relevance
10/15/2026
First Seen
Valid until: 1/13/2027
Sources
Related Entities
Related Claims
UnverifiedA new study generates concise if-then rules and uses Group Relative Policy Optimization (GRPO) to train Qwen2.5 and Llama3 series models, evaluating the results on four ToM benchmarks.76% similarUnverifiedThe research trains a deep recursive model using various rule combinations and tests its performance at four different levels of novelty.72% similarUnverifiedGrok 4.5 completes endpoint training on the basis of data cleaning and high-quality filtering combined with reinforcement learning, in order to improve long-term reasoning ability in real engineering projects.71% similarUnverifiedGRPO天然适合可验证任务,在数学、代码等场景可用规则化奖励替代复杂奖励模型69% similarUnverifiedD-GRS generates K conditionally independent candidates at each node and tests them sequentially in generation order, with the first candidate that passes the test being selected.69% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/1038137API
curl https://kongchang.com/api/v1/knowledge/claims/1038137MCP
get_claim(id=1038137)