Tencent / Tencent/digitalhuman
Question about baselines of RLVMR (ReAct implementation and evaluation code)
Nobody has claimed this yet.
- Dominant language
- Python
- Stars
- 361
- Forks
- 52
- PR merge metrics
- No merged PRs in 30d
Description
Hello, thank you for the great work on the RLVMR. I'm particularly interested in the ReAct results mentioned in the experiments (Qwen-1.5B/7B ReAct).
I noticed that your experimental setup and evaluation environment likely differ from the official ReAct implementation. Could you please clarify if the code for the prompting ReAct (without any fine-tuning) and its evaluation pipeline are included in this repository?
Contributor guide
No contributing guide indexed for this repository
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
Review the repository's experiment and evaluation materials, then compare them with the official ReAct implementation. Determine whether prompting-only ReAct for Qwen-1.5B/7B and its evaluation pipeline are included, and document the relevant locations or clarify their absence.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python
- Domain
- machine-learning
- Issue type
- Documentation
- Difficulty
- 3/5
- Estimated time
- 1-2 days
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100