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Research2026-07-18 · source-backed
Turing Post's survey lays out the consolidated three-stage pipeline for tool-using agents: supervised fine-tuning for format and cold start, preference optimization (DPO/SimPO) for alignment, then RL with verifiable rewards (GRPO/DAPO) for reasoning and generalization. GRPO's practical draw is that it drops the value critic entirely and estimates advantages by group-wise comparison, so only the ordering of completions matters. Scores of 0.3/0.5/0.7 and 30/50/70 train identically. That makes hand-written reward functions dramatically more forgiving of bad scaling, which is where most homegrown reward code goes wrong.
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Unsloth released GRPO / Shared entity: GRPO / Shared topic / Earlier coverage / Tension
Linked by a graph relationship (Unsloth released GRPO); both cover GRPO; overlapping topics (critic, grpo).
Unsloth released GRPO
Linked by a graph relationship (Unsloth released GRPO).
Linked by a graph relationship (Unsloth released GRPO).
GRPO competes with PPO / Shared entity: GRPO / Shared topic / Earlier coverage
Linked by a graph relationship (GRPO competes with PPO); both cover GRPO; overlapping topics (agent, grpo).
DPO competes with RLHF / Shared entity: DPO / Earlier coverage / Downstream implication
Linked by a graph relationship (DPO competes with RLHF); both cover DPO; earlier DPO coverage from 2026-05-11.
DPO competes with RLHF / Shared entity: DPO / Earlier coverage
Linked by a graph relationship (DPO competes with RLHF); both cover DPO; earlier DPO coverage from 2026-06-30.
DPO competes with RLHF / Shared topic / Tension
Linked by a graph relationship (DPO competes with RLHF); overlapping topics (alignment, dramatically); pushes against this story (but).
Unsloth released GRPO
Linked by a graph relationship (Unsloth released GRPO).