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Shannon Sands 2025-05-16 19:49:37 -07:00
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environments/README.md
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@ -241,6 +241,7 @@ Two Blackjack environment implementations are provided. For more details, see th
- **Gameplay:** A more complex version designed for agents that produce long interaction sequences, including "thinking" steps.
- **Features:** Windowed decision making, local alternative generation, value-based pruning, and counterfactual data for training (GRPO).
- **Use Case:** Ideal for training LLMs that engage in explicit multi-step reasoning before action. Teaches the model to be more "confident" about selecting optimal moves & taking informed risks in uncertain environments, even with the knowledge that it might still lose with optimal play.
### Instruction Following Environment (`instruction_following_algorithm_environment.py`)
**Dependencies:**
@ -252,9 +253,6 @@ This environment was inspired by AllenAI's RLVR-IFEVAL environment and uses Alle
- Paper: https://arxiv.org/abs/2411.15124
Environment for training models to follow natural language instructions and constraints, based on the `allenai/RLVR-IFeval` dataset and environment.
**Dependencies:**
- `datasets` (Hugging Face)
- `langdetect`
**Input Format:**
- Each item from the processed `allenai/RLVR-IFeval` dataset contains: