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Releases: OpenMLRL/LLM_Collab_Code_Generation

v1.3.9

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@LovelyBuggies LovelyBuggies released this 02 Jul 17:43

v.1.3.8

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@LovelyBuggies LovelyBuggies released this 02 Jul 17:28
07663c1

Corresponds to CoMLRL v1.3.8.

v.1.3.6

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@LovelyBuggies LovelyBuggies released this 13 Feb 23:25
492310c

This version provides the coding collaboration environments for CoMLRL v1.3.6 with aligned interfaces.

Changelog

  • Fixed critical error in hetero model loading, and allows more flexible critics and agents (see docs/model-loading).
  • Change downstreaming repos' interfaces accordingly (align them into v1.3.6) and polish docs.

v1.3.2

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@LovelyBuggies LovelyBuggies released this 29 Jan 15:00

Changelog

Update this environment and align with CoMLRL 1.3.2.

CoopHE

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v1.3.1

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@LovelyBuggies LovelyBuggies released this 30 Dec 18:24
cfbda2e

Changelog

  • Allow batch training in MAGRPOTrainer, IACTrainer and MAACTrainer
  • Allow multi-turn training in IACTrainer and MAACTrainer
  • Change the x-axis from data_step to env_step
  • Pair with CoMLRL v1.3.1
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v1.1.7

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@LovelyBuggies LovelyBuggies released this 31 Oct 16:20

Remove the memory mode in 1.1.6, just using the full history.

60% succ (12/20).

v1.1.6

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@LovelyBuggies LovelyBuggies released this 29 Oct 13:58
e95e44f

Update 3 memory modes. Should work together with CoMLRL's v1.1.6.

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v1.1.5

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@LovelyBuggies LovelyBuggies released this 15 Oct 21:12

Ryan fixed the joint mode for single-agent case, and I changed the readme

v1.1.4

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@LovelyBuggies LovelyBuggies released this 05 Oct 14:47

Clean up unused variables in v1.1.3. Code logic has not changed.

Screenshot 2025-10-06 at 12 20 35

v1.1.3

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@LovelyBuggies LovelyBuggies released this 05 Oct 00:46
d775bec

This version should work with CoMLRL v1.1.3.

Changelog

  1. Remove the hard-coded code-level-logging at each node, since we don't expect users to inspect the details during training, at the cost of huge VRAM usage.
  2. Change the default value of hyperparameters according to the Dr. GRPO style, and learning rate to be 2e-5 and no more bandit external mode since it is equivalent to the magrpo in single-turn.
  3. Optimize the code format and group the closed params together.
  4. Add MBPP dataset.
image Plain fails 2; expert fails 1; level feedback not fail yet.