Researchers have introduced a new framework called SELF (SELF-distilLation with environmental Feedback modeling) to improve language agents trained in interactive environments where direct rewards are unavailable. This framework jointly optimizes environmental feedback modeling and hindsight self-distillation, enabling agents to predict environmental responses while learning from a feedback-conditioned self-teacher. Experiments show that SELF outperforms existing methods like SDPO and GRPO on benchmarks such as tau-Bench and AppWorld, demonstrating its effectiveness in enhancing agent capabilities by more efficiently utilizing environmental feedback. AI
IMPACT Enhances agent capabilities in environments lacking direct rewards, potentially improving performance in complex interactive tasks.
RANK_REASON The cluster contains an academic paper detailing a new framework and experimental results for training language agents. [lever_c_demoted from research: ic=1 ai=1.0]
- Agentic hindsight self-distillation
- Agentic SElf-distilLation with environmental Feedback modeling
- AppWorld
- arXiv
- Environmental feedback modeling
- Grpo
- Hindsight Self-Distillation
- Qwen3_8B
- reinforcement learning
- SELF
- Self Distillation Using Contrastive Evidence Policy Optimization
- tau-Bench
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