qijiashen/e2e-terminal-rl-qwen35-4b-slime-sb-XpAvCqbu2T1wSFs1zgsNlP
The qijiashen/e2e-terminal-rl-qwen35-4b-slime-sb-XpAvCqbu2T1wSFs1zgsNlP model is a 2.3 billion parameter model with a 32768 token context length, developed by qijiashen. It is an ungraded backup of a benchmark agent's submission, specifically designed for reinforcement learning in terminal environments. This model's primary use case is within the agentrl-bench framework, focusing on evaluating and improving agent performance in complex interactive tasks.
Loading preview...
Overview
This model, qijiashen/e2e-terminal-rl-qwen35-4b-slime-sb-XpAvCqbu2T1wSFs1zgsNlP, is a 2.3 billion parameter model with a 32768 token context length. It represents an ungraded backup of a submission within the agentrl-bench framework, indicating its role in evaluating and developing reinforcement learning agents.
Key Capabilities
- Reinforcement Learning Agent Submission: Functions as a submission for the
agentrl-benchbenchmark, suggesting its use in competitive RL environments. - Terminal Environment Focus: Implies specialization in tasks that involve interacting with terminal-based interfaces.
- Performance Tracking: The submission includes performance metrics such as
dev 27/128 vs base 8/64andcomplex 5/48 vs 1/48, indicating its evaluation against a baseline in development and complex scenarios.
Good for
- Benchmarking RL Agents: Ideal for researchers and developers working on the
agentrl-benchplatform to submit and evaluate their reinforcement learning agents. - Terminal-based Automation: Potentially useful for tasks requiring automated interaction within command-line or terminal environments.
- Agent Development and Iteration: Provides a snapshot of an agent's performance at a specific iteration (
run6 iter38), which can be valuable for tracking progress and debugging.