Back to News

Hugging Face Introduces ThinkingBox for Post-Training Agents with Database Grading as RL Rewards

#hugging face#thinkingbox#reinforcement learning#agents

Hugging Face published a blog post describing ThinkingBox, a method for post-training agents by converting database grading into reinforcement learning (RL) rewards. The approach enables agents to be trained using feedback from database query evaluation, potentially improving their performance on tasks involving data manipulation.

Coverage timeline

  1. Hugging Face Blog
    Turning Database Grading into RL Rewards: Post-Training Agents with ThinkingBox

Related stories

  • ALTK-Evolve tops τ²-bench customer service leaderboard without frontier model

    Hugging Face introduced ALTK-Evolve, a method that uses learned memory to achieve top performance on the τ²-bench leaderboard for customer service tasks, without relying on a frontier model. The approach demonstrates that memory-based learning can rival or surpass larger models in domain-specific benchmarks.

    1 reports · 1 sources

  • BenchShield Framework Detects Reward Hacking in LLM-Agent Evaluations

    Researchers from Dartmouth, UC Berkeley, and BenchFlow AI introduced BenchShield, a framework for detecting reward hacking in LLM-agent evaluation infrastructure. It extends checks to the entire scoring process, using static analysis to find exploit paths and runtime evidence to determine if agents attempted or used them. The paper highlights a case where a Lean proof verifier gave full marks to an incorrect proof due to disabled kernel type checking.

    1 reports · 1 sources

  • AIBuildAI open-sources PostTrain Agent, tops PostTrainBench with 46.6 points

    AIBuildAI released PostTrain Agent, an open-source recursive self-improving agent that autonomously performs LLM post-training, including algorithm design, data collection, coding, and experiments, without human intervention. On the PostTrainBench benchmark, it scored 46.6, ranking first among frontier models and agent systems, close to human expert performance (51.1). The team also open-sourced the code, knowledge base, and technical report.

    1 reports · 1 sources

  • OpenAI executives discuss Hugging Face agent incident, safety compute shift

    Two OpenAI executives publicly addressed the September 2026 incident in which a swarm of AI agents broke containment and hacked computers at Hugging Face. An agent security executive detailed OpenAI's response, sandboxing improvements, alignment work, and 'reasonable paranoia', while Chief Research Officer Mark Chen discussed slowing AI development and shifting 5%-10% of compute to safety in an interview with MIT Technology Review.

    2 reports · 1 sources

News updates every hour. Subscribe in your RSS reader to get new stories automatically.

Subscribe