@neural_avb: Locally generating GRPO-like rollouts with my SLM, and using this tiny RM as the rubric. Next I'll be RL training on fr…
Summary
Neural_avb releases a lightweight Answer-eq Reward Model for RL training on QA tasks, claiming 80% agreement with external judge LM and faster than F1/ROUGE/BertScore.
View Cached Full Text
Cached at: 06/12/26, 04:54 AM
Locally generating GRPO-like rollouts with my SLM, and using this tiny RM as the rubric. Next I’ll be RL training on free form text and QA.
This is
- super fast
- way better than F1/ROGUE/BertScore
- 80% agreement with external judge LM (deepseek)
RL with Unverifiable Rewards!
Yeah! For this one I got GPT to write some rich… I’d normally won’t bother with all these pretty printing/streaming, but since this will all go in a YT video in the end… making things look aesthetically pleasing is one of the side missions.
Let me think about it! I need to first crystallize this philosophy myself
Similar Articles
@neural_avb: Lurking the Reasoning Training docs rn. Time to write a verifiers env and Unsloth/TRL that shit! Video soon if it all g…
The user is working on implementing reasoning training with verifiers using Unsloth and TRL, reporting progress on locally generating GRPO-like rollouts with a small SLM and a tiny RM, and promises a video soon.
@neural_avb: This post-training article came out earlier this year and completely flew under my radar. Highly recommended for my GRP…
A recommendation of a post-training article on GRPO/RLVR that was overlooked earlier this year, aimed at those interested in reinforcement learning from verifiable rewards.
@neural_avb: https://x.com/neural_avb/status/2063907440509571354
Explores a common failure mode in recursive language models (RLMs) where free-text subagent responses cause issues, and presents a solution using structured outputs to improve reliability, illustrated with a long-context question-answering example from NarrativeQA.
Cross-Epoch Adaptive Rollout Optimization for RL Post-Training
This paper presents CERO, a cross-epoch adaptive rollout optimization method for RL post-training of LLMs, which allocates a fixed rollout budget across prompts and epochs using Bayesian posterior variance to maximize sample efficiency, achieving theoretical regret bounds and outperforming GRPO on mathematical reasoning tasks.
@RyanBoldi: Your RL post-training may be sabotaging your LLM’s test-time scaling! Conventional RL pretends that you can collapse al…
Introduces Vector Policy Optimization (VPO), a new RL method that handles vector-valued rewards to improve test-time scaling for LLMs, outperforming conventional scalar reward approaches.