self-rewarding

Implementation of the training framework proposed in Self-Rewarding Language Model, from MetaAI

SQL-o1: A Self-Reward Heuristic Dynamic Search Method for Text-to-SQL

Reinforcement Learning of Vision Language Models with Self Visual Perception Reward

SeRL: Self-Play Reinforcement Learning for Large Language Models with Limited Data

Consistent Paths Lead to Truth: Self-Rewarding Reinforcement Learning for LLM Reasoning

An official implementation of "SPARK: Synergistic Policy And Reward Co-Evolving Framework"

GenPark AI Agent Skill - Iterative self-rewarding LLM judge scoring multi-turn agent outputs and synthesizing contrastive hard-negative prompt variations.

GenPark AI Agent Skill - Direct Preference Optimization (DPO) implicit reward calculation, reference policy log-ratio tracking, and pairwise preference loss evaluation.

GenPark AI Agent Skill - Prioritized experience replay buffer for agent reinforcement fine-tuning (RFT / GRPO) with advantage estimation and importance sampling weights.

GenPark AI Agent Skill - Pairwise Bradley-Terry and Elo rating tournament engine for ranking multi-agent strategies, prompt mutations, and generated tool solutions.

GenPark AI Agent Skill - Direct Preference Optimization (DPO) implicit reward calculation, reference policy log-ratio tracking, and pairwise preference loss evaluation.

GenPark AI Agent Skill - Pairwise Bradley-Terry and Elo rating tournament engine for ranking multi-agent strategies, prompt mutations, and generated tool solutions.

GenPark AI Agent Skill - Kahneman-Tversky Optimization (KTO) loss evaluator aligning agents directly on unpaired binary feedback (thumbs-up / thumbs-down) using prospect theory loss aversion.

GenPark AI Agent Skill - Iterative self-rewarding LLM judge scoring multi-turn agent outputs and synthesizing contrastive hard-negative prompt variations.

GenPark AI Agent Skill - Prioritized experience replay buffer for agent reinforcement fine-tuning (RFT / GRPO) with advantage estimation and importance sampling weights.

GenPark AI Agent Skill - Kahneman-Tversky Optimization (KTO) loss evaluator aligning agents directly on unpaired binary feedback (thumbs-up / thumbs-down) using prospect theory loss aversion.

Class-Conditional self-reward mechanism for improved Text-to-Image models

Does a model training on its own judgment collapse? On verifiable math: the reward gets hacked (a brevity reward halves answer length) but capability does not collapse. An honest failure analysis with a judge-quality probe and per-round trajectory datasets.